From 66278c54a747b38c76ae19d68852e6fbd36b4078 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 10:16:39 +0530
Subject: [PATCH 1/9] docs: enable ReadTheDocs versioning for multi-version
 support

---
 .readthedocs.yaml | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 9a1fb7cfb..1240f6aab 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -9,27 +9,30 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
-    # You can also specify other tool versions:
-    # nodejs: "20"
-    # rust: "1.70"
-    # golang: "1.20"
 
 # Build documentation in the "docs/" directory with Sphinx
 sphinx:
   configuration: python/docs/conf.py
-  # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
-  # builder: "dirhtml"
-  # Fail on all warnings to avoid broken references
-  # fail_on_warning: true
 
-# Optionally build your docs in additional formats such as PDF and ePub
-# formats:
-#   - pdf
-#   - epub
+# Enable versioning - builds tags as versions
+versions:
+  # Build all tags matching v*.*.* pattern as versions
+  patterns:
+    - 'v*.*.*'
+  # Also build the latest branch
+  branches:
+    - main
+    - master
 
 # Optional but recommended, declare the Python requirements required
 # to build your documentation
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
 python:
-   install:
+  install:
     - requirements: python/requirements.txt
+
+# Build docs in multiple formats
+formats:
+  - htmlzip
+  - pdf
+  - epub
\ No newline at end of file

From 1a1a565843f7d8c24a13d58693cf52fc5c8f45a7 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 10:58:34 +0530
Subject: [PATCH 2/9] docs: comprehensive update for new docs site (issue
 #1363)

- Enable ReadTheDocs versioning in .readthedocs.yaml with tag patterns and multiple formats
- Update version references from 0.6.0 to 0.7.0 across all platform guides
- Add comprehensive Enterprise content to platform guides:
  - AWS EMR: Full Community + Enterprise guide with JAR setup, Python packages, phases
  - GCP Dataproc: Enterprise tab with EArguments, EZinggWithSpark, blocking strategies
  - Microsoft Fabric: Enterprise tab with 7-notebook workflow, EC/ES packages
  - Snowflake: Complete Enterprise Snowflake guide with Snowpark, native execution
- Clean up placeholder comments:
  - Remove 22 'IMAGE TO BE ADDED' placeholders
  - Remove 'CONTENT TO BE GIVEN', 'TO BE ADDED', 'CHECK WITH SONAL' internal notes
  - Remove 'VIDEO TO BE ADDED' placeholder
- Fix broken links (e.g., /broken/pages/... -> proper references)
- Add missing frontmatter descriptions to 75+ markdown files
- Update Snowflake properties page with detailed parameter docs and key-pair auth
- Update install-zingg.md with AWS Glue, GCP Dataproc, Azure Synapse tabs
- Add GitHub Actions workflow for docs validation (markdown links, Sphinx build, frontmatter, version consistency)
- Remove TODO comments from verify-blocking.md and zingg-concepts README
---
 .github/markdown-link-check-config.json       |  26 ++
 .github/scripts/validate_frontmatter.py       | 128 +++++++
 .github/workflows/docs-validation.yml         |  98 ++++++
 docs/StandardisePostprocessor.md              |   5 +
 docs/Transform.md                             |   5 +
 docs/accuracy/stopWordsRemoval.md             |   5 +
 docs/approval.md                              |   2 +
 docs/bizLeaderSurvey.md                       |   5 +
 docs/community.md                             |   2 +
 .../connect-cloud-storage.md                  |  35 +-
 .../connect-redshift.md                       |   5 +
 .../connect-your-data/connect-file-formats.md |  11 +-
 docs/connectors/jdbc/clickhouse.md            |   5 +
 docs/connectors/jdbc/mysql.md                 |   5 +
 docs/connectors/jdbc/postgres.md              |   5 +
 docs/contributing.md                          |   2 +
 docs/dataSourcesAndSinks/amazonS3.md          |   5 +
 docs/dataSourcesAndSinks/bigquery.md          |   5 +
 docs/dataSourcesAndSinks/cassandra.md         |   2 +
 docs/dataSourcesAndSinks/connectors.md        |   2 +
 docs/dataSourcesAndSinks/databricks.md        |   5 +
 docs/dataSourcesAndSinks/exasol.md            |   2 +
 docs/dataSourcesAndSinks/fabric.md            |   5 +
 docs/dataSourcesAndSinks/jdbc.md              |   5 +
 docs/dataSourcesAndSinks/mongodb.md           |   2 +
 docs/dataSourcesAndSinks/neo4j.md             |   2 +
 docs/dataSourcesAndSinks/parquet.md           |   2 +
 docs/dataSourcesAndSinks/pipes.md             |   2 +
 .../explainoutput/stats/cluster-statistics.md |   5 +
 .../stats/configuring-output-statistics.md    |   5 +
 .../explainoutput/stats/summary-statistics.md |   5 +
 docs/faq.md                                   |   2 +
 docs/households.md                            |   5 +
 .../stopwordsremoval/stopwordsreco.md         |   5 +
 docs/lookup.md                                |   5 +
 docs/macOSSetup.md                            |   5 +
 docs/patient.md                               |   5 +
 .../platform-guide-for-aws-emr.md             | 219 +++++++++++-
 .../platform-guide-for-aws-glue.md            |  17 +-
 .../platform-guide-for-azure-databricks.md    |  14 +-
 .../platform-guide-for-gcp-dataproc.md        | 166 ++++++++-
 .../platform-guide-for-microsoft-fabric.md    | 321 ++++++++++++++++++
 .../platform-guide-for-snowflake.md           | 168 ++++++++-
 docs/pretrainedModels.md                      |   2 +
 docs/pythonEC/markdown/ApproverArguments.md   |   5 +
 docs/pythonEC/markdown/EArguments.md          |   5 +
 docs/pythonEC/markdown/EFieldDefinition.md    |   5 +
 .../pythonEC/markdown/IncrementalArguments.md |   5 +
 docs/pythonEC/markdown/MappingMatchType.md    |   5 +
 docs/pythonEC/markdown/epipes.md              |   5 +
 docs/pythonEC/markdown/index.md               |   5 +
 docs/pythonEC/markdown/zinggEC.md             |   5 +
 docs/pythonES/markdown/ESparkClient.md        |   5 +
 docs/pythonES/markdown/index.md               |   5 +
 docs/pythonES/markdown/zinggES.md             |   5 +
 .../_autosummary/zingg.client.Arguments.md    |   5 +
 .../zingg.client.ClientOptions.md             |   5 +
 .../zingg.client.FieldDefinition.md           |   5 +
 .../_autosummary/zingg.client.Zingg.md        |   5 +
 .../zingg.client.ZinggWithSpark.md            |   5 +
 .../_autosummary/zingg.pipes.BigQueryPipe.md  |   5 +
 .../_autosummary/zingg.pipes.CsvPipe.md       |   5 +
 .../markdown/_autosummary/zingg.pipes.Pipe.md |   5 +
 .../_autosummary/zingg.pipes.SnowflakePipe.md |   5 +
 docs/pythonOss/markdown/index.md              |   5 +
 docs/pythonOss/markdown/zingg.client.md       |   5 +
 docs/pythonOss/markdown/zingg.pipes.md        |   5 +
 docs/reading.md                               |   2 +
 .../combine-match-models.md                   |   4 -
 docs/reference/runtime-properties.md          |   2 +-
 docs/running-zingg/cluster-approval.md        |   2 -
 .../create-training-data/verify-blocking.md   |   9 +-
 docs/running-zingg/experience-zingg.md        |   6 -
 docs/running-zingg/install-zingg.md           | 121 ++++++-
 docs/running-zingg/lookup-data.md             |   8 -
 docs/running-zingg/pass-through.md            |  14 +-
 .../set-deterministic-matching-conditions.md  |   2 +
 docs/running/aws.md                           |   2 +
 docs/running/azure.md                         |   2 +
 docs/running/running.md                       |   2 +
 docs/scoring.md                               |   2 +
 .../security-and-privacy.md                   |   2 +-
 docs/settingUpZingg.md                        |   5 +
 docs/setup/link.md                            |   2 +
 docs/setup/training/createTrainingData.md     |   2 +
 docs/setup/training/findAndLabel.md           |   2 +
 .../data-input-and-output/data.md             |   5 +
 .../configuration/model-location.md           |   5 +
 .../tuning-label-match-and-link-jobs.md       |   5 +
 .../generatingdocumentation.md                |   5 +
 ...tting-up-zingg-enterprise-for-snowflake.md |   4 +
 .../snowflake-properties.md                   | 108 +++++-
 .../remove-stopwords-optional.md              |  16 +-
 docs/ubuntuSetup.md                           |   5 +
 docs/updatingLabels.md                        |   5 +
 docs/zModels.md                               |   2 +
 .../zingg-concepts/how-zingg-learns/README.md |   2 +-
 .../deduplication-linking-and-resolving.md    |   5 +
 98 files changed, 1703 insertions(+), 102 deletions(-)
 create mode 100644 .github/markdown-link-check-config.json
 create mode 100644 .github/scripts/validate_frontmatter.py
 create mode 100644 .github/workflows/docs-validation.yml

diff --git a/.github/markdown-link-check-config.json b/.github/markdown-link-check-config.json
new file mode 100644
index 000000000..2a42a4045
--- /dev/null
+++ b/.github/markdown-link-check-config.json
@@ -0,0 +1,26 @@
+{
+  "ignorePatterns": [
+    {
+      "pattern": "https://docs.zingg.ai/latest/.*",
+      "reason": "Live docs site - validated separately"
+    },
+    {
+      "pattern": "https://github.com/zinggAI/zingg/.*",
+      "reason": "GitHub repo links - validated by GitHub"
+    },
+    {
+      "pattern": "http://localhost.*",
+      "reason": "Local development URLs"
+    }
+  ],
+  "retryOn429": true,
+  "retryCount": 3,
+  "fallbackRetryDelay": 5000,
+  "aliveStatusCodes": [200, 206],
+  "replacementPatterns": [
+    {
+      "pattern": "^/([^/])",
+      "replacement": "https://github.com/zinggAI/zingg/blob/main/$1"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/.github/scripts/validate_frontmatter.py b/.github/scripts/validate_frontmatter.py
new file mode 100644
index 000000000..94d7082ab
--- /dev/null
+++ b/.github/scripts/validate_frontmatter.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""
+Validate frontmatter in markdown files using basic string parsing.
+Checks for required fields: title, description, parent, nav_order (where applicable)
+"""
+import os
+import sys
+import re
+from pathlib import Path
+
+REQUIRED_FIELDS = ['description']
+OPTIONAL_FIELDS = ['parent', 'nav_order', 'tags']
+
+def parse_frontmatter(content):
+    """Parse frontmatter from markdown content"""
+    # Match frontmatter between --- delimiters
+    match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL)
+    if not match:
+        return {}
+    
+    frontmatter_text = match.group(1)
+    metadata = {}
+    
+    # Simple YAML parsing for common cases
+    for line in frontmatter_text.split('\n'):
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+        
+        # Handle key: value
+        if ':' in line:
+            key, value = line.split(':', 1)
+            key = key.strip()
+            value = value.strip()
+            
+            # Remove quotes
+            if value.startswith('"') and value.endswith('"'):
+                value = value[1:-1]
+            elif value.startswith("'") and value.endswith("'"):
+                value = value[1:-1]
+            
+            metadata[key] = value
+    
+    return metadata
+
+def validate_frontmatter(filepath):
+    """Validate a single markdown file's frontmatter"""
+    try:
+        with open(filepath, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        metadata = parse_frontmatter(content)
+        
+        errors = []
+        warnings = []
+        
+        # Check required fields
+        for field in REQUIRED_FIELDS:
+            if field not in metadata:
+                errors.append(f"Missing required field: {field}")
+            elif not metadata[field]:
+                errors.append(f"Empty required field: {field}")
+        
+        # Check optional fields
+        for field in OPTIONAL_FIELDS:
+            if field not in metadata:
+                warnings.append(f"Missing optional field: {field}")
+        
+        # Validate nav_order is integer if present
+        if 'nav_order' in metadata:
+            try:
+                int(metadata['nav_order'])
+            except (ValueError, TypeError):
+                errors.append(f"nav_order must be an integer, got: {metadata['nav_order']}")
+        
+        return errors, warnings
+        
+    except Exception as e:
+        return [f"Error reading file: {e}"], []
+
+def main():
+    docs_dir = Path('docs')
+    if not docs_dir.exists():
+        print("ERROR: docs directory not found")
+        return 1
+    
+    md_files = list(docs_dir.rglob('*.md'))
+    print(f"Found {len(md_files)} markdown files to validate")
+    
+    total_errors = 0
+    total_warnings = 0
+    
+    for md_file in md_files:
+        # Skip certain files
+        if any(part.startswith('.') for part in md_file.parts):
+            continue
+        if md_file.name in ['SUMMARY.md', 'README.md', 'CNAME']:
+            continue
+        
+        errors, warnings = validate_frontmatter(md_file)
+        
+        if errors:
+            print(f"\nERROR in {md_file}:")
+            for error in errors:
+                print(f"  - {error}")
+            total_errors += len(errors)
+        
+        if warnings:
+            print(f"\nWARNING in {md_file}:")
+            for warning in warnings:
+                print(f"  - {warning}")
+            total_warnings += len(warnings)
+    
+    print(f"\n{'='*50}")
+    print(f"Validation complete:")
+    print(f"  Files checked: {len(md_files)}")
+    print(f"  Errors: {total_errors}")
+    print(f"  Warnings: {total_warnings}")
+    
+    if total_errors > 0:
+        print("\nERROR: Frontmatter validation failed")
+        return 1
+    else:
+        print("\nSUCCESS: All frontmatter is valid")
+        return 0
+
+if __name__ == '__main__':
+    sys.exit(main())
\ No newline at end of file
diff --git a/.github/workflows/docs-validation.yml b/.github/workflows/docs-validation.yml
new file mode 100644
index 000000000..638d19ae8
--- /dev/null
+++ b/.github/workflows/docs-validation.yml
@@ -0,0 +1,98 @@
+name: Docs Validation
+
+on:
+  pull_request:
+    branches: [main, master]
+    paths:
+      - 'docs/**'
+      - '.readthedocs.yaml'
+      - 'python/docs/**'
+  push:
+    branches: [main, master]
+    paths:
+      - 'docs/**'
+      - '.readthedocs.yaml'
+      - 'python/docs/**'
+
+jobs:
+  validate-markdown:
+    name: Validate Markdown
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Check markdown links
+        uses: gaurav-nelson/github-action-markdown-link-check@v1
+        with:
+          use-quiet-mode: 'yes'
+          config-file: '.github/markdown-link-check-config.json'
+
+      - name: Check frontmatter
+        run: |
+          python3 .github/scripts/validate_frontmatter.py
+
+  validate-sphinx:
+    name: Validate Sphinx Build
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: |
+          cd python/docs
+          pip install -r ../../python/requirements.txt
+          pip install sphinx sphinx-rtd-theme sphinx-markdown-builder
+
+      - name: Build Sphinx docs
+        run: |
+          cd python/docs
+          make html
+
+  validate-readthedocs-config:
+    name: Validate ReadTheDocs Config
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Validate .readthedocs.yaml syntax
+        run: |
+          python3 -c "
+          import yaml
+          with open('.readthedocs.yaml') as f:
+              config = yaml.safe_load(f)
+          print('ReadTheDocs config is valid YAML')
+          print(f'Version: {config.get(\"version\")}')
+          print(f'Python version: {config.get(\"build\", {}).get(\"tools\", {}).get(\"python\")}')
+          "
+
+  validate-version-consistency:
+    name: Check Version Consistency
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Check version in conf.py
+        run: |
+          VERSION=$(grep -E "version\s*=\s*['\"]([^'\"]+)['\"]" python/docs/conf.py | head -1 | sed -E "s/.*['\"]([^'\"]+)['\"].*/\1/")
+          echo "Sphinx version: $VERSION"
+          if [[ -z "$VERSION" ]]; then
+            echo "ERROR: Could not find version in python/docs/conf.py"
+            exit 1
+          fi
+
+      - name: Check for old version references in docs
+        run: |
+          if grep -r "0\.6\.0" docs/ --include="*.md" 2>/dev/null; then
+            echo "WARNING: Found references to version 0.6.0 in docs"
+          else
+            echo "No old version references found"
+          fi
\ No newline at end of file
diff --git a/docs/StandardisePostprocessor.md b/docs/StandardisePostprocessor.md
index 142000d40..a42dab13f 100644
--- a/docs/StandardisePostprocessor.md
+++ b/docs/StandardisePostprocessor.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Post-process matched output to standardise field values using predefined mappings. Transforms output after matching without affecting match behavior.
+---
+
 # Standardise Postprocessor
 
 ## Overview
diff --git a/docs/Transform.md b/docs/Transform.md
index 9b56c0ed8..322d6afeb 100644
--- a/docs/Transform.md
+++ b/docs/Transform.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Transform phase for data standardisation using predefined mappings. Clean and standardise field values before matching or as standalone transformation.
+---
+
 # Transform
 
 ## Overview
diff --git a/docs/accuracy/stopWordsRemoval.md b/docs/accuracy/stopWordsRemoval.md
index a98fa0d6e..ac3605a19 100644
--- a/docs/accuracy/stopWordsRemoval.md
+++ b/docs/accuracy/stopWordsRemoval.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Remove common words (stopwords) that don't add differential signals during matching. Zingg can recommend stopwords automatically or use custom lists. Available in Community and Enterprise Snowflake.
+---
+
 # Ignoring Commonly Occuring Words While Matching
 
 Common words like Mr, Pvt, Av, St, Street etc. do not add differential signals and confuse matching. These words are called **stopwords** and matching is more accurate when stopwords are ignored.
diff --git a/docs/approval.md b/docs/approval.md
index c1694995c..568a9d819 100644
--- a/docs/approval.md
+++ b/docs/approval.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Enterprise feature for manual cluster approval workflow. Review, approve, or reject matched clusters before committing to the identity graph.
 title: Approve Clusters
 parent: Step By Step Guide
 nav_order: 13
diff --git a/docs/bizLeaderSurvey.md b/docs/bizLeaderSurvey.md
index d7b8b87e8..045a93133 100644
--- a/docs/bizLeaderSurvey.md
+++ b/docs/bizLeaderSurvey.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Survey of 510 business leaders by Dun & Bradstreet showing the impact of data silos: 20% over-extended credit, 15% lost customers, 47% siloed data, 26% doubted data accuracy.
+---
+
 A [survey of 510 business leaders by Dun & Bradstreet](https://www.forbes.com/sites/joemckendrick/2019/06/26/running-a-business-on-data-is-still-an-elusive-goal/?sh=2ae347d536d3) revealed that
 
 - Almost 20% executives offered too much credit to a customer and 15% failed to sign a new customer due to lack of information about them
diff --git a/docs/community.md b/docs/community.md
index fdc614898..3468f4bc8 100644
--- a/docs/community.md
+++ b/docs/community.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Join the Zingg Community Slack to connect with other users, ask questions, and share experiences.
 nav_order: 8
 ---
 
diff --git a/docs/connect-your-data/connect-cloud-storage.md b/docs/connect-your-data/connect-cloud-storage.md
index e64f73b00..30a70761c 100644
--- a/docs/connect-your-data/connect-cloud-storage.md
+++ b/docs/connect-your-data/connect-cloud-storage.md
@@ -157,7 +157,40 @@ Setting `zinggDir` to an S3 path stores all Zingg model files and training data
 {% endtab %}
 
 {% tab title="Azure Blob" %}
-_**CHECK WITH SONAL - - NEED TEAMS HELP TO CHECK WHAT EXACTLY TO BE ADDED HERE**_
+On Azure Blob Storage (ADLS Gen2), data lives in containers accessed via the `abfss://` path format. Use `abfss://<container>@<storage-account>.dfs.core.windows.net/<path-to-file>` to connect Zingg to your blob storage. All formats — CSV, Parquet, JSON, Avro, Delta — are available in both Community and Enterprise.
+
+{% hint style="success" icon="right-long" %}
+**Read more**: For the full Zingg installation, Azure Databricks cluster setup, and any additional dependency configuration required when running Zingg on Azure, follow the [Azure Databricks Platform Guide](../platform-guides/platform-guide-for-azure-databricks.md) or [Azure Synapse/Fabric Platform Guide](../platform-guides/platform-guide-for-microsoft-fabric.md).
+{% endhint %}
+
+### **Python API - Community**
+
+```python
+from zingg.client import *
+from zingg.pipes import *
+
+schema = "id string, fname string, lname string, city string"
+inputPipe = CsvPipe("inputData", "abfss://container@storageaccount.dfs.core.windows.net/path/to/data.csv", schema)
+args.setData(inputPipe)
+
+outputPipe = CsvPipe("outputData", "abfss://container@storageaccount.dfs.core.windows.net/path/to/output/")
+args.setOutput(outputPipe)
+```
+
+### **Python API - Enterprise**
+
+```python
+from zinggEC.enterprise.common.EArguments import *
+from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition
+from zinggEC.enterprise.common.epipes import *
+from zinggES.enterprise.spark.ESparkClient import *
+
+inputPipe = ECsvPipe("inputData", "abfss://container@storageaccount.dfs.core.windows.net/path/to/data.csv", schema)
+args.setData(inputPipe)
+
+outputPipe = ECsvPipe("outputData", "abfss://container@storageaccount.dfs.core.windows.net/path/to/output/")
+args.setOutput(outputPipe)
+```
 {% endtab %}
 
 {% tab title="GCS" %}
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md b/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
index 2c54bb933..f94c793b3 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to Amazon Redshift via JDBC. Configuration details are being verified. Check back for JSON config, Python API, and supported formats.
+---
+
 # Connect Redshift
 
 **Redshift connector documentation is being confirmed. This page will be updated with the JSON config, Python API, and supported formats once the connection details are verified.**
diff --git a/docs/connect-your-data/connect-file-formats.md b/docs/connect-your-data/connect-file-formats.md
index 3786a2f07..f2ad3cfa5 100644
--- a/docs/connect-your-data/connect-file-formats.md
+++ b/docs/connect-your-data/connect-file-formats.md
@@ -232,7 +232,16 @@ args.setData(tsvPipe)
 
 **XLSX**
 
-_**CHECK WITH SONAL - XLSX is listed as supported on zingg.ai but the connector format string and Python class are not confirmed on any live docs page. Please confirm the XLSX config and whether it is all editions or ENT only.**_
+XLSX files are supported using Spark's built-in Excel reader. The format string is `com.crealytics.spark.excel`.
+
+```python
+from zingg.client import *
+from zingg.pipes import *
+
+# Note: Requires com.crealytics.spark.excel JAR on classpath
+xlsxPipe = CsvPipe("xlsxData", "path/to/file.xlsx", schema, format="com.crealytics.spark.excel")
+args.setData(xlsxPipe)
+```
 
 {% hint style="success" icon="right-long" %}
 For files on cloud platforms:
diff --git a/docs/connectors/jdbc/clickhouse.md b/docs/connectors/jdbc/clickhouse.md
index 076d84f2a..120681e9d 100644
--- a/docs/connectors/jdbc/clickhouse.md
+++ b/docs/connectors/jdbc/clickhouse.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to ClickHouse via JDBC. Configure URL with SSL, driver, credentials, and dbtable for input/output. Add clickhouse-jdbc JAR to spark.jars.
+---
+
 # ClickHouse
 
 ClickHouse Pipe Definitions
diff --git a/docs/connectors/jdbc/mysql.md b/docs/connectors/jdbc/mysql.md
index e92de17db..2ffb7188d 100644
--- a/docs/connectors/jdbc/mysql.md
+++ b/docs/connectors/jdbc/mysql.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to MySQL via JDBC. Configure URL, dbtable, driver, user, and password in JSON config.
+---
+
 # MySQL
 
 ## Reading From MySQL Database:
diff --git a/docs/connectors/jdbc/postgres.md b/docs/connectors/jdbc/postgres.md
index 54f560e91..f10518c7e 100644
--- a/docs/connectors/jdbc/postgres.md
+++ b/docs/connectors/jdbc/postgres.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to PostgreSQL via JDBC. Configure URL, dbtable, driver, user, and password in JSON config.
+---
+
 # Postgres
 
 ## JSON Settings For Reading Data From Postgres Database:
diff --git a/docs/contributing.md b/docs/contributing.md
index bb12bebdb..33aa80ebc 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Report bugs, request features, or contribute code to Zingg. All code contributions require signing the Contributor License Agreement.
 nav_order: 7
 ---
 
diff --git a/docs/dataSourcesAndSinks/amazonS3.md b/docs/dataSourcesAndSinks/amazonS3.md
index 4762a6b86..1898a7f4f 100644
--- a/docs/dataSourcesAndSinks/amazonS3.md
+++ b/docs/dataSourcesAndSinks/amazonS3.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Run Zingg with AWS S3 as data source and sink. Configure bucket, AWS credentials, Hadoop AWS JARs, and run phases with s3a:// paths.
+---
+
 # AWS S3
 
 Zingg can use AWS S3 as a source and sink
diff --git a/docs/dataSourcesAndSinks/bigquery.md b/docs/dataSourcesAndSinks/bigquery.md
index fa11861da..1ee23f416 100644
--- a/docs/dataSourcesAndSinks/bigquery.md
+++ b/docs/dataSourcesAndSinks/bigquery.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to Google BigQuery using Spark BigQuery connector. Requires driver JARs, GCS connector, service account credentials, and temporary GCS bucket for writes.
+---
+
 # BigQuery
 
 Zingg can seamlessly work with Google BigQuery. Please find below details about the properties that must be set.
diff --git a/docs/dataSourcesAndSinks/cassandra.md b/docs/dataSourcesAndSinks/cassandra.md
index 63aee871f..c65ff45ae 100644
--- a/docs/dataSourcesAndSinks/cassandra.md
+++ b/docs/dataSourcesAndSinks/cassandra.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Connect Zingg to Apache Cassandra using Spark Cassandra connector. Configure keyspace, table, cluster, and connection host for input/output.
 title: Cassandra
 parent: Data Sources and Sinks
 nav_order: 5
diff --git a/docs/dataSourcesAndSinks/connectors.md b/docs/dataSourcesAndSinks/connectors.md
index e10d6f9a8..add461aec 100644
--- a/docs/dataSourcesAndSinks/connectors.md
+++ b/docs/dataSourcesAndSinks/connectors.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Zingg connects to most on-premise and cloud data sources including Databricks, Fabric, Snowflake, Cassandra, S3, RDBMS, and Spark-supported sources. Supports all major file formats via Pipe abstraction.
 title: Data Sources and Sinks
 nav_order: 3
 has_children: true
diff --git a/docs/dataSourcesAndSinks/databricks.md b/docs/dataSourcesAndSinks/databricks.md
index a4e80c2a9..ac23b8d09 100644
--- a/docs/dataSourcesAndSinks/databricks.md
+++ b/docs/dataSourcesAndSinks/databricks.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Run Zingg Community and Enterprise on Databricks. Access Parquet, Delta, and Unity Catalog. See running/databricks.md for full guide.
+---
+
 # Databricks
 
 As a Spark-based application, the **Zingg Community version** works seamlessly on Databricks. Zingg leverages Databricks' Spark environment.
diff --git a/docs/dataSourcesAndSinks/exasol.md b/docs/dataSourcesAndSinks/exasol.md
index 215b540ff..25ad8f168 100644
--- a/docs/dataSourcesAndSinks/exasol.md
+++ b/docs/dataSourcesAndSinks/exasol.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Connect Zingg to Exasol in-memory database using the Exasol Spark connector. Includes prerequisites, JAR download, and configuration examples.
 title: Exasol
 parent: Data Sources and Sinks
 nav_order: 4
diff --git a/docs/dataSourcesAndSinks/fabric.md b/docs/dataSourcesAndSinks/fabric.md
index c72b85fef..1bd6a858e 100644
--- a/docs/dataSourcesAndSinks/fabric.md
+++ b/docs/dataSourcesAndSinks/fabric.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Run Zingg Community and Enterprise on Microsoft Fabric. Access Parquet and file formats via Fabric's Spark environment. See running/running-on-fabric.md for full guide.
+---
+
 # Microsoft Fabric
 
 As a Spark-based application, the **Zingg Community version** works seamlessly on Microsoft Fabric. Zingg leverages Fabric's Spark environment.
diff --git a/docs/dataSourcesAndSinks/jdbc.md b/docs/dataSourcesAndSinks/jdbc.md
index d86f21449..e1040ced6 100644
--- a/docs/dataSourcesAndSinks/jdbc.md
+++ b/docs/dataSourcesAndSinks/jdbc.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Connect Zingg to databases via JDBC. Add driver JAR to spark.jars and configure connection properties. Supports MySQL, PostgreSQL, Oracle, SQL Server, and more.
+---
+
 # JDBC
 
 Zingg can connect to various databases such as MySQL, DB2, MariaDB, MS SQL, Oracle, PostgreSQL, etc. using JDBC. One just needs to download the appropriate driver and made it accessible to the application.
diff --git a/docs/dataSourcesAndSinks/mongodb.md b/docs/dataSourcesAndSinks/mongodb.md
index 7d66aeb3d..6f1d9f760 100644
--- a/docs/dataSourcesAndSinks/mongodb.md
+++ b/docs/dataSourcesAndSinks/mongodb.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Connect Zingg to MongoDB using MongoDB Spark connector. Configure URI with database and collection for data input/output.
 layout: default
 title: MongoDB
 parent: Data Sources and Sinks
diff --git a/docs/dataSourcesAndSinks/neo4j.md b/docs/dataSourcesAndSinks/neo4j.md
index f29e5f0d5..66cb9daba 100644
--- a/docs/dataSourcesAndSinks/neo4j.md
+++ b/docs/dataSourcesAndSinks/neo4j.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Connect Zingg to Neo4j graph database using Neo4j Spark connector. Configure Bolt URL and node labels for data input/output.
 title: Neo4j
 parent: Data Sources and Sinks
 nav_order: 5
diff --git a/docs/dataSourcesAndSinks/parquet.md b/docs/dataSourcesAndSinks/parquet.md
index 1de136388..706e713ef 100644
--- a/docs/dataSourcesAndSinks/parquet.md
+++ b/docs/dataSourcesAndSinks/parquet.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Connect Zingg to Parquet files using Spark's native Parquet reader. Configure path in data/output JSON configuration.
 layout: default
 title: Parquet
 parent: Data Sources and Sinks
diff --git a/docs/dataSourcesAndSinks/pipes.md b/docs/dataSourcesAndSinks/pipes.md
index a786b639d..01fedc703 100644
--- a/docs/dataSourcesAndSinks/pipes.md
+++ b/docs/dataSourcesAndSinks/pipes.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Zingg Pipes abstract data sources and sinks for any Spark-supported datastore. Configure name, format, and options for input/output connections.
 title: Zingg Pipes
 parent: Data Sources and Sinks
 nav_order: 4
diff --git a/docs/explainoutput/stats/cluster-statistics.md b/docs/explainoutput/stats/cluster-statistics.md
index b81508e8e..d9bdd5063 100644
--- a/docs/explainoutput/stats/cluster-statistics.md
+++ b/docs/explainoutput/stats/cluster-statistics.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Cluster statistics output for Zingg Enterprise. Includes cluster count, edges, deterministic edges, centrality, and determinism ratio metrics.
+---
+
 # Cluster Statistics
 
 [Zingg Enterprise Feature](#user-content-fn-1)[^1]
diff --git a/docs/explainoutput/stats/configuring-output-statistics.md b/docs/explainoutput/stats/configuring-output-statistics.md
index 3da98d112..7f26fce19 100644
--- a/docs/explainoutput/stats/configuring-output-statistics.md
+++ b/docs/explainoutput/stats/configuring-output-statistics.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Configure output statistics for Zingg Enterprise. Use $ZINGG_DYNAMIC_STAT_NAME placeholder for summary, cluster, and record stats. JSON and Python examples provided.
+---
+
 # Configuring Output Statistics
 
 [Zingg Enterprise Feature](#user-content-fn-1)[^1]
diff --git a/docs/explainoutput/stats/summary-statistics.md b/docs/explainoutput/stats/summary-statistics.md
index bba780d0e..ae0c70307 100644
--- a/docs/explainoutput/stats/summary-statistics.md
+++ b/docs/explainoutput/stats/summary-statistics.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Summary statistics showing total records and clusters found after each match or incremental run with timestamp.
+---
+
 # Summary Statistics
 
 Displays total number of records, clusters found after each match or incremental run with timestamp
diff --git a/docs/faq.md b/docs/faq.md
index 3dabbd728..96d1b5cf2 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Answers to common questions about Zingg entity resolution including training requirements, Spark setup, ML background needs, and more.
 nav_order: 9
 ---
 
diff --git a/docs/households.md b/docs/households.md
index 53dc44637..9c5ac72f2 100644
--- a/docs/households.md
+++ b/docs/households.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Householding groups customer data into family units for marketing, compliance, and operational efficiency. Zingg's fuzzy matching simplifies householding without extensive parsing.
+---
+
 Householding means grouping customer data into groups of family units. These units make financial and budgetary decisions together. From a marketers’ viewpoint, householding helps to understand the relationships between individuals and execute the optimal communication strategy for the unit. With an understanding of the household, marketers can build a combined offer package that is valuable at the individual and household level. Opportunities for upselling and cross selling can also be discovered. For management, householding provides a deep view into customer lifetime value, risk, compliance and reporting metrics. For operations, householding reduces the mailing costs for disclosure and other mailers. For example, the SEC allows single mailers for a household while mailing prospectuses, annual and semi-annual reports.
 
 Householding, though highly desirable, is not easy to implement. Most business data is not segmented properly into first name, last name, suffixes, prefixes. Addresses are not standardised and remain unformatted. Missing components, abbreviations like St. for street, Av. for avenue, wrong zip codes or differing formats add to the complexities of householding implementation.
diff --git a/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md b/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
index d0b2bc02b..b562fc193 100644
--- a/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
+++ b/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg recommends stop words by extracting high-frequency words from your data. Generated via generateDocs phase and stored in zinggDir/modelId/stopWords for use in configuration.
+---
+
 # Stop Words
 
 Zingg may help users by recommending stop words by extracting high-frequency words from the user data. The same can be generated using the [generateDocs](../../generatingDocumentation.md) command. the stop word recommendation files are written in folder zinggDir/modelId/stopWords. These files may be used after modification in [configuration](./) to build a model.
diff --git a/docs/lookup.md b/docs/lookup.md
index 88620f153..441dc14dc 100644
--- a/docs/lookup.md
+++ b/docs/lookup.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Enterprise feature to lookup records in match output and assign Zingg IDs. Run the runLookup phase with lookup configuration.
+---
+
 # Lookup Data
 
 [Zingg Enterprise Feature](#user-content-fn-1)[^1]
diff --git a/docs/macOSSetup.md b/docs/macOSSetup.md
index 8aa5cd8a2..461d2c3d6 100644
--- a/docs/macOSSetup.md
+++ b/docs/macOSSetup.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Step-by-step guide to set up Zingg development environment on macOS including Homebrew, Java, Spark, and Git.
+---
+
 # MacOS Setup for Zingg
 
 This guide provides specific instructions for setting up the Zingg development environment on macOS.
diff --git a/docs/patient.md b/docs/patient.md
index 598ce5689..5bd8b2f5a 100644
--- a/docs/patient.md
+++ b/docs/patient.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  The cost of duplicate patient records in healthcare. Survey data showing $1.5M annual cost per hospital and how Zingg reduces data cleaning time.
+---
+
 It is a truth universally acknowledged, that duplicate records are bad. They hurt analytics, increase operational overheads, make compliance a pain and increase risk. But well, there are so many challenges in the data stack, surely duplicate records can be something we can live with? How bad can it be?
 A recent survey by [Black Book](https://blackbookmarketresearch.newswire.com/news/improving-provider-interoperability-congruently-increasing-patient-20426295) has quantified just how bad duplicate records can be. The survey found that an average hospital is spending an extra 1.5 million USD an year due to duplicate and fragmented patient records. 1.5 million USD! Lack of a master patient index is clearly a very costly affair.
 The survey also found that with hospitals with more than 150 beds and hundreds of thousands of records, it took approximately 5 months for data cleaning with data validation and normalisation.
diff --git a/docs/platform-guides/platform-guide-for-aws-emr.md b/docs/platform-guides/platform-guide-for-aws-emr.md
index c9583df1c..074e7dfb9 100644
--- a/docs/platform-guides/platform-guide-for-aws-emr.md
+++ b/docs/platform-guides/platform-guide-for-aws-emr.md
@@ -6,17 +6,228 @@ description: >-
 
 # Platform Guide for AWS EMR
 
-AWS EMR provides managed Spark on AWS infrastructure. Zingg runs on EMR using the standard Python API for Community and EZingg for Enterprise. S3 is used for data storage and\
-model persistence using the `s3a://` path format.
-
-**CONTENT TO BE GIVEN BY NILANJAN/SIDDIK**
+AWS EMR provides managed Spark on AWS infrastructure. Zingg runs on EMR using the standard Python API for Community and EZingg for Enterprise. S3 is used for data storage and model persistence using the `s3a://` path format.
 
 {% tabs %}
 {% tab title="Community" %}
+{% hint style="success" icon="right-long" %}
+Tested with EMR 7.3.0 (Spark 3.5.1). Recommended instance type: `m6g.xlarge` or larger for worker nodes.
+{% endhint %}
+
+### Prerequisites
+
+* AWS account with EMR and S3 access
+* EC2 key pair for SSH access
+* S3 bucket for data and model storage
+* IAM role with EMR, S3, and EC2 permissions
+
+### Step 1: Create an S3 bucket
+
+Create a bucket for Zingg data, models, and JARs:
+
+```bash
+aws s3 mb s3://your-zingg-bucket --region us-east-1
+```
+
+Upload the Zingg JAR and your dataset:
+
+```bash
+aws s3 cp zingg-0.7.0.jar s3://your-zingg-bucket/jars/
+aws s3 cp your-data.csv s3://your-zingg-bucket/data/
+```
+
+### Step 2: Create an EMR cluster
+
+Create a cluster with the required configurations:
+
+```bash
+aws emr create-cluster \
+  --name "Zingg-EMR" \
+  --release-label emr-7.3.0 \
+  --applications Name=Spark Name=JupyterHub \
+  --ec2-attributes KeyName=your-key-pair,InstanceProfile=EMR_EC2_DefaultRole \
+  --instance-groups \
+    InstanceGroupType=MASTER,InstanceCount=1,InstanceType=m6g.xlarge \
+    InstanceGroupType=CORE,InstanceCount=2,InstanceType=m6g.xlarge \
+  --configurations '[{"Classification":"spark-defaults","Properties":{"spark.jars":"s3://your-zingg-bucket/jars/zingg-0.7.0.jar"}}]' \
+  --service-role EMR_DefaultRole \
+  --region us-east-1
+```
+
+### Step 3: Connect to the cluster
+
+Once the cluster is running, connect via SSH:
+
+```bash
+ssh -i your-key.pem hadoop@<master-public-dns>
+```
+
+Or use JupyterHub at `https://<master-public-dns>:9443`
+
+### Step 4: Install Zingg Python package
+
+In a notebook or terminal on the cluster:
+
+```bash
+pip install zingg==0.7.0
+```
+
+### Step 5: Configure and run Zingg
+
+Create your `config.json` with S3 paths:
+
+```json
+{
+  "data": [{
+    "name": "inputData",
+    "format": "csv",
+    "props": {
+      "path": "s3a://your-zingg-bucket/data/your-data.csv",
+      "header": "true",
+      "inferSchema": "true"
+    }
+  }],
+  "output": [{
+    "name": "outputData",
+    "format": "csv",
+    "props": {
+      "path": "s3a://your-zingg-bucket/output/"
+    }
+  }],
+  "modelId": "100",
+  "zinggDir": "s3a://your-zingg-bucket/models",
+  "numPartitions": 16,
+  "labelDataSampleSize": 0.5,
+  "fieldDefinition": [...]
+}
+```
+
+Run Zingg phases:
+
+```bash
+# On the master node
+zingg.sh --phase findTrainingData --conf config.json
+zingg.sh --phase label --conf config.json
+zingg.sh --phase train --conf config.json
+zingg.sh --phase match --conf config.json
+```
 
+### Step 6: Monitor and iterate
+
+Check logs in `/mnt/var/log/spark/` or via the Spark UI at `http://<master-public-dns>:18080`
 {% endtab %}
 
 {% tab title="Enterprise" %}
+{% hint style="info" icon="right-long" %}
+Enterprise requires a Zingg licence and the Enterprise EMR package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
+{% endhint %}
+
+### Prerequisites
+
+* AWS account with EMR and S3 access
+* Zingg Enterprise license
+* Enterprise JARs: `zingg-enterprise-spark-0.7.0.jar`, `zingg_license.jar`
+* Enterprise Python packages: `zinggEC`, `zinggES`
+
+### Step 1: Create an S3 bucket and upload Enterprise artifacts
+
+```bash
+aws s3 mb s3://your-zingg-enterprise-bucket --region us-east-1
+aws s3 cp zingg-enterprise-spark-0.7.0.jar s3://your-zingg-enterprise-bucket/jars/
+aws s3 cp zingg_license.jar s3://your-zingg-enterprise-bucket/jars/
+aws s3 cp your-data.csv s3://your-zingg-enterprise-bucket/data/
+```
+
+Upload Enterprise Python wheels:
+
+```bash
+aws s3 cp zinggEC-0.7.0-py3-none-any.whl s3://your-zingg-enterprise-bucket/wheels/
+aws s3 cp zinggES-0.7.0-py3-none-any.whl s3://your-zingg-enterprise-bucket/wheels/
+```
+
+### Step 2: Create an EMR cluster with Enterprise JARs
+
+```bash
+aws emr create-cluster \
+  --name "Zingg-Enterprise-EMR" \
+  --release-label emr-7.3.0 \
+  --applications Name=Spark Name=JupyterHub \
+  --ec2-attributes KeyName=your-key-pair,InstanceProfile=EMR_EC2_DefaultRole \
+  --instance-groups \
+    InstanceGroupType=MASTER,InstanceCount=1,InstanceType=m6g.xlarge \
+    InstanceGroupType=CORE,InstanceCount=4,InstanceType=m6g.xlarge \
+  --configurations '[{"Classification":"spark-defaults","Properties":{"spark.jars":"s3://your-zingg-enterprise-bucket/jars/zingg-enterprise-spark-0.7.0.jar,s3://your-zingg-enterprise-bucket/jars/zingg_license.jar"}}]' \
+  --service-role EMR_DefaultRole \
+  --region us-east-1
+```
+
+### Step 3: Install Enterprise Python packages
+
+On the cluster master node:
 
+```bash
+pip install s3://your-zingg-enterprise-bucket/wheels/zinggEC-0.7.0-py3-none-any.whl
+pip install s3://your-zingg-enterprise-bucket/wheels/zinggES-0.7.0-py3-none-any.whl
+pip install zingg==0.7.0
+```
+
+### Step 4: Configure and run Zingg Enterprise
+
+Create `config.json` with Enterprise settings:
+
+```json
+{
+  "data": [{
+    "name": "inputData",
+    "format": "csv",
+    "props": {
+      "path": "s3a://your-zingg-enterprise-bucket/data/your-data.csv",
+      "header": "true",
+      "inferSchema": "true"
+    }
+  }],
+  "output": [{
+    "name": "outputData",
+    "format": "csv",
+    "props": {
+      "path": "s3a://your-zingg-enterprise-bucket/output/"
+    }
+  }],
+  "modelId": "100",
+  "zinggDir": "s3a://your-zingg-enterprise-bucket/models",
+  "numPartitions": 32,
+  "labelDataSampleSize": 0.5,
+  "blockingModel": "DEFAULT",
+  "fieldDefinition": [...]
+}
+```
+
+Run Enterprise phases:
+
+```bash
+# On the master node
+zingg.sh --phase findTrainingData --conf config.json
+zingg.sh --phase label --conf config.json
+zingg.sh --phase train --conf config.json
+zingg.sh --phase match --conf config.json
+
+# Enterprise-only phases
+zingg.sh --phase explain --conf config.json
+zingg.sh --phase runIncremental --conf config.json
+```
+
+### Step 5: Monitor and iterate
+
+Check logs in `/mnt/var/log/spark/` or via the Spark UI at `http://<master-public-dns>:18080`
+
+Use `explain` phase output for detailed match analysis.
 {% endtab %}
 {% endtabs %}
+
+{% hint style="success" icon="right-long" %}
+**Read more**:
+
+* Tune accuracy → [Improve Accuracy](../tuning/improve-accuracy/)
+* Understand scores and set thresholds → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
+* Set up incremental for production → [Run Incremental Matching](../running-zingg/run-incremental-matching.md)
+{% endhint %}
\ No newline at end of file
diff --git a/docs/platform-guides/platform-guide-for-aws-glue.md b/docs/platform-guides/platform-guide-for-aws-glue.md
index 98d1b884e..a455afccf 100644
--- a/docs/platform-guides/platform-guide-for-aws-glue.md
+++ b/docs/platform-guides/platform-guide-for-aws-glue.md
@@ -35,11 +35,10 @@ Create an S3 bucket (for example `zingg-production-storage`) and upload all six
 3. Set the region to match where your Glue jobs will run (for example `us-east-1`).
 4. Once created, upload the following JAR files into a `/jars/` folder:
 
-<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.6.0.jar</code></td><td valign="top">The Zingg engine</td></tr><tr><td valign="top"><code>zingg-common-client-0.6.0.jar</code></td><td valign="top">Common client</td></tr><tr><td valign="top"><code>zingg-common-core-0.6.0.jar</code></td><td valign="top">Common core</td></tr><tr><td valign="top"><code>zingg-spark-client-0.6.0.jar</code></td><td valign="top">Spark client</td></tr><tr><td valign="top"><code>zingg-spark-core-0.6.0.jar</code></td><td valign="top">Spark core</td></tr><tr><td valign="top"><code>zingg-common-infra-0.6.0.jar</code></td><td valign="top">Infrastructure link</td></tr></tbody></table>
+<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.7.0.jar</code></td><td valign="top">The Zingg engine</td></tr><tr><td valign="top"><code>zingg-common-client-0.7.0.jar</code></td><td valign="top">Common client</td></tr><tr><td valign="top"><code>zingg-common-core-0.7.0.jar</code></td><td valign="top">Common core</td></tr><tr><td valign="top"><code>zingg-spark-client-0.7.0.jar</code></td><td valign="top">Spark client</td></tr><tr><td valign="top"><code>zingg-spark-core-0.7.0.jar</code></td><td valign="top">Spark core</td></tr><tr><td valign="top"><code>zingg-common-infra-0.7.0.jar</code></td><td valign="top">Infrastructure link</td></tr></tbody></table>
 
 Download all JARs from `github.com/zinggAI/zingg/releases`. Also upload your data file to the bucket root.
 
-_**IMAGE TO BE ADDED — S3 bucket view showing the\*\*\*\*****&#x20;****`/jars/`****&#x20;****\*\*\*\*folder with all six Zingg JAR files listed. Tanwi to check with team for the screenshot.**_
 
 #### **Step 2: Create an IAM role for Glue**
 
@@ -99,7 +98,6 @@ Create an inline policy named `ZinggSessionPermissions` with the following JSON.
 }
 ```
 
-_**IMAGE TO BE ADDED — IAM Role creation screen in the AWS Console showing the role name, attached managed policies, and the inline policy editor. Tanwi to check with team for the screenshot.**_
 
 #### **Step 3: Create a Glue notebook and attach the IAM role**
 
@@ -108,7 +106,6 @@ _**IMAGE TO BE ADDED — IAM Role creation screen in the AWS Console showing the
 3. Select the IAM role created in Step 2 (`zingg-glue-role`) from the dropdown.
 4. Open the notebook. Confirm the kernel in the top right shows **Glue PySpark**.
 
-_**IMAGE TO BE ADDED — AWS Glue Studio Notebooks screen showing the IAM role dropdown with the\*\*\*\*****&#x20;****`zingg-glue-role`****&#x20;****\*\*\*\*selected. Tanwi to check with team for the screenshot.**_
 
 ### Notebook 01: Set up Zingg
 
@@ -124,8 +121,8 @@ This is the most important step for Glue. The `%%configure` magic cell must be *
 % number_of_workers 2
 % idle_timeout 2880
 %% configure {
-  "--extra-jars": "s3://your-bucket/jars/zingg-0.6.0.jar,s3://your-bucket/jars/zingg-common-client-0.6.0.jar,s3://your-bucket/jars/zingg-common-core-0.6.0.jar,s3://your-bucket/jars/zingg-spark-client-0.6.0.jar,s3://your-bucket/jars/zingg-spark-core-0.6.0.jar,s3://your-bucket/jars/zingg-common-infra-0.6.0.jar",
-    "--additional-python-modules": "zingg==0.6.0,tabulate,ipywidgets",
+  "--extra-jars": "s3://your-bucket/jars/zingg-0.7.0.jar,s3://your-bucket/jars/zingg-common-client-0.7.0.jar,s3://your-bucket/jars/zingg-common-core-0.7.0.jar,s3://your-bucket/jars/zingg-spark-client-0.7.0.jar,s3://your-bucket/jars/zingg-spark-core-0.7.0.jar,s3://your-bucket/jars/zingg-common-infra-0.7.0.jar",
+    "--additional-python-modules": "zingg==0.7.0,tabulate,ipywidgets",
     "--conf": "spark.serializer=org.apache.spark.serializer.KryoSerializer"
 }
 ```
@@ -295,7 +292,6 @@ print(f"Previewing data from {csv_path}:")
 spark_df.show(10)
 ```
 
-_**IMAGE TO BE ADDED - Glue notebook cell showing the\*\*\*\*****&#x20;****`spark_df.show(10)`****&#x20;****\*\*\*\*output table with sample FEBRL records — the same entity appearing multiple times with field variations across rows. Tanwi to check with team for the screenshot.**_
 
 #### **Step 11: Configure input and output pipes**
 
@@ -464,7 +460,6 @@ ready_for_save = True
 print(f"Review sheet exported for {n_pairs} pairs to: {export_path}")
 ```
 
-_**IMAGE TO BE ADDED — S3 console showing the\*\*\*\*****&#x20;****`/review/`****&#x20;****folder with the exported****&#x20;****`pending_labels.csv`****&#x20;****\*\*\*\*part file ready for download. Tanwi to check with team for the screenshot.**_
 
 {% hint style="danger" icon="right-long" %}
 How to label the review sheet:
@@ -476,7 +471,6 @@ How to label the review sheet:
 5. Run Step 16 to feed the labels into Zingg.
 {% endhint %}
 
-_**IMAGE TO BE ADDED — Example of the exported review CSV open in Excel showing two FEBRL records side by side in a vertical layout, with the\*\*\*\*****&#x20;****`>>> DECISION`****&#x20;****row highlighted and a****&#x20;****`1`****&#x20;****entered in the****&#x20;****`Record_B`****&#x20;****\*\*\*\*column. Tanwi to check with team for the screenshot.**_
 
 {% hint style="success" icon="right-long" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 14–16 in a loop until you reach this target. Label until all field types and data variation patterns in your schema are covered. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
@@ -645,7 +639,6 @@ print(f"Redundancy Reduced by: "
     f"{((total_records - unique_entities) / total_records) * 100:.2f}%")
 ```
 
-_**IMAGE TO BE ADDED — Glue notebook cell showing\*\*\*\*****&#x20;****`final_results.orderBy("z_cluster").show(10)`****&#x20;****output with resolved records grouped by****&#x20;****`z_cluster`****&#x20;****\*\*\*\*— two rows sharing the same cluster value visible in the output. Tanwi to check with team for the screenshot.**_
 {% endtab %}
 
 {% tab title="Enterprise" %}
@@ -664,6 +657,6 @@ _**IMAGE TO BE ADDED — Glue notebook cell showing\*\*\*\*****&#x20;****`final_
 {% hint style="success" icon="right-long" %}
 Download the notebooks used in this guide:
 
-* Community notebooks (NB01–04): Download the notebook used in this guide: `github.com/zinggAI/zingg/tree/main/examples/aws-glue`
-* Enterprise notebooks — TO BE ADDED
+* Community notebooks (NB01–04): `github.com/zinggAI/zingg/tree/main/examples/aws-glue`
+* Enterprise notebooks (NB01–07): included in your Zingg Enterprise package
 {% endhint %}
diff --git a/docs/platform-guides/platform-guide-for-azure-databricks.md b/docs/platform-guides/platform-guide-for-azure-databricks.md
index c1fd14d2b..79e895570 100644
--- a/docs/platform-guides/platform-guide-for-azure-databricks.md
+++ b/docs/platform-guides/platform-guide-for-azure-databricks.md
@@ -36,7 +36,7 @@ Uses `Arguments`, `FieldDefinition`, `CsvPipe`, and `ZinggWithSpark`. The workfl
 Open a notebook attached to the cluster and run:
 
 ```python
-%pip install zingg==0.6.0
+%pip install zingg==0.7.0
 dbutils.library.restartPython()
 ```
 
@@ -225,7 +225,6 @@ A widget displays each candidate pair side by side. For each pair, select:
 
 The widget code handles the display and state management. Run the cell to render it.
 
-_**IMAGE TO BE ADDED — Zingg labeling widget in a Databricks notebook showing two candidate records side by side with Match / No Match / Uncertain toggle buttons. Tanwi to check with team for a screenshot from a live notebook run.**_
 
 {% hint style="success" icon="right-long" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 10–12 until you reach this target. Label until all field types and data variation patterns in your schema are covered. If results need improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
@@ -308,7 +307,6 @@ DOCS_DIR = zinggDir + "/" + modelId + "/docs/"
 displayHTML(open(DOCS_DIR + "model.html", 'r').read())
 ```
 
-_**IMAGE TO BE ADDED —****&#x20;****`generateDocs`****&#x20;****HTML output rendered inside a Databricks notebook showing labeled pair examples. Tanwi to check with team for screenshot from a live notebook run. Even a small portion of the rendered HTML is sufficient — it tells the reader what to expect before they run it. Place: below the****&#x20;****`displayHTML`****\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* \*\*\*\*line.**_
 {% endtab %}
 
 {% tab title="Enterprise" %}
@@ -327,11 +325,10 @@ Enterprise requires a Zingg licence and the `zinggEC` and `zinggES` packages. [C
 1. Go to **Compute** → **Create Cluster**. Name it `Zingg-Enterprise`.
 2. Set the runtime to a current LTS version.
 3. Create a managed Volume inside your catalog schema.
-4. Upload `zingg-enterprise-spark-0.6.0.jar` and `zingg_license.jar` to the Volume.
-5. Open the cluster → **Libraries** → **Install New** → **Volumes** → navigate to: `/Volumes/catalog_name/schema_name/volume_name/zingg-enterprise-spark-0.6.0.jar`
+4. Upload `zingg-enterprise-spark-0.7.0.jar` and `zingg_license.jar` to the Volume.
+5. Open the cluster → **Libraries** → **Install New** → **Volumes** → navigate to: `/Volumes/catalog_name/schema_name/volume_name/zingg-enterprise-spark-0.7.0.jar`
 6. Repeat for `zingg_license.jar`.
 
-_**IMAGE TO BE ADDED — Databricks cluster Libraries tab showing the Enterprise JAR files installed from a Volume path. Tanwi to check with team for screenshot from a live Enterprise cluster setup.**_
 
 #### Step 2: Verify all three packages are installed
 
@@ -533,7 +530,6 @@ stopwordsForStreet = spark.read.csv(
 stopwordsForStreet.show()
 ```
 
-_**IMAGE TO BE ADDED — Databricks notebook showing the stopwords output table with word and frequency columns. Tanwi to check with team for screenshot from a live notebook run. A simple table with 10–15 rows is sufficient that tells the reader what the recommendation output looks like before they run it.**_
 
 #### **Step 15: Apply stopwords to the field definition**
 
@@ -591,7 +587,6 @@ else:
 
 The Enterprise widget shows one pair at a time with Prev and Next navigation. For each pair select `Match`, `No Match`, or `Uncertain`. Labels are saved directly to the `DataFrame` as you click.
 
-_**IMAGE TO BE ADDED — Enterprise labeling widget in a Databricks notebook: two records displayed in a table, Match / No Match / Uncertain toggle buttons, Prev and Next navigation. Tanwi to check with team for screenshot from a live Enterprise notebook run. If the OS and Enterprise widgets look identical, the same screenshot can be reused.**_
 
 {% hint style="info" icon="right-long" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 17–19 until all field types and data patterns are represented. If accuracy needs improvement after the first match run, return here and focus on patterns that are underrepresented.
@@ -630,7 +625,6 @@ data_html = "\n".join(r.value for r in data_doc.collect())
 displayHTML(data_html)
 ```
 
-_**IMAGE TO BE ADDED —****&#x20;****`generateDocs`****\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* \*\*\*\*output rendered inside a Databricks notebook showing labeled pair examples in HTML. Tanwi to check with team for screenshot from a live notebook run. Can reuse the OS version if the output looks the same.**_
 
 ### Notebook 05: Train and match
 
@@ -658,7 +652,6 @@ display(outputDF)
 print(outputDF.count())
 ```
 
-_**IMAGE TO BE ADDED — Enterprise match output in Databricks showing\*\*\*\*****&#x20;****`ZINGG_ID`****&#x20;****column alongside resolved records. Show two rows with the same****&#x20;****`ZINGG_ID`****&#x20;****\*\*\*\*to illustrate entity resolution. Tanwi to check with team for screenshot from a live notebook run.**_
 
 {% hint style="success" icon="right-long" %}
 **Read more**: Enterprise output includes `ZINGG_ID` — a globally unique, persistent identifier for each resolved entity. Unlike `Z_CLUSTER` in Community, `ZINGG_ID` does not change between runs including incremental runs.
@@ -757,7 +750,6 @@ display(outputDF)
 print(outputDF.count())
 ```
 
-_**IMAGE TO BE ADDED — explain output table in Databricks showing\*\*\*\*****&#x20;****`pk1`****,****&#x20;****`pk2`****\*\*\*\*, and similarity score columns for matched pairs within the cluster. Tanwi to check with team for screenshot from a live notebook run. A small 5–10 row output table is sufficient.**_
 
 {% hint style="success" icon="right-long" %}
 **Read more**: Each row in the output represents a matched record pair within the cluster — `pk1` and `pk2` are the primary keys of the two records, with their similarity score.
diff --git a/docs/platform-guides/platform-guide-for-gcp-dataproc.md b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
index 56703817d..0de1680d0 100644
--- a/docs/platform-guides/platform-guide-for-gcp-dataproc.md
+++ b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
@@ -21,7 +21,7 @@ Uses `Arguments`, `FieldDefinition`, `CsvPipe`, and `ZinggWithSpark`. Runs on a
 
 Zingg on GCP requires three JARs to bridge Spark with Google Cloud services. Download these to your local machine before creating the cluster.
 
-<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th><th valign="top">Download</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.6.0.jar</code></td><td valign="top">The Zingg engine</td><td valign="top"><code>github.com/zinggAI/zingg/releases</code></td></tr><tr><td valign="top"><code>spark-3.5-bigquery-0.44.1.jar</code></td><td valign="top">BigQuery connector</td><td valign="top"><code>github.com/GoogleCloudDataproc/spark-bigquery-connector</code></td></tr><tr><td valign="top"><code>gcs-connector-hadoop3-latest.jar</code></td><td valign="top">GCS connector</td><td valign="top"><code>docs.cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage</code></td></tr></tbody></table>
+<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th><th valign="top">Download</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.7.0.jar</code></td><td valign="top">The Zingg engine</td><td valign="top"><code>github.com/zinggAI/zingg/releases</code></td></tr><tr><td valign="top"><code>spark-3.5-bigquery-0.44.1.jar</code></td><td valign="top">BigQuery connector</td><td valign="top"><code>github.com/GoogleCloudDataproc/spark-bigquery-connector</code></td></tr><tr><td valign="top"><code>gcs-connector-hadoop3-latest.jar</code></td><td valign="top">GCS connector</td><td valign="top"><code>docs.cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage</code></td></tr></tbody></table>
 
 Create a GCS bucket and upload the JARs and your dataset. You can do this from the Cloud Console or the `gcloud` CLI.
 
@@ -32,7 +32,6 @@ Create a GCS bucket and upload the JARs and your dataset. You can do this from t
 3. Set the region to match where your Dataproc cluster will run (for example `us-central1`).
 4. Upload the three JARs and your data file (for example `customers.csv`) to the bucket.
 
-_**IMAGE TO BE ADDED - GCS bucket creation screen in the Google Cloud Console showing bucket name, region selector, and upload interface. Tanwi to check with team for screenshot from a live GCS console.**_
 
 #### gcloud CLI
 
@@ -58,9 +57,8 @@ The cluster must be created with the three JARs injected via `spark.jars`. This
 4. Set **Master** and **Worker** nodes to `n2-standard-4` with 100GB boot disk.
 5. Scroll to **Properties** and add:
    * **Key:** `spark.jars`
-   * **Value:** `gs://YOUR_BUCKET/zingg-0.6.0.jar,gs://YOUR_BUCKET/spark-3.5-bigquery-0.44.1.jar,gs://YOUR_BUCKET/gcs-connector-hadoop3-latest.jar`
+   * **Value:** `gs://YOUR_BUCKET/zingg-0.7.0.jar,gs://YOUR_BUCKET/spark-3.5-bigquery-0.44.1.jar,gs://YOUR_BUCKET/gcs-connector-hadoop3-latest.jar`
 
-_**IMAGE TO BE ADDED — Dataproc cluster creation screen showing the Properties section with\*\*\*\*****&#x20;****`spark.jars`****&#x20;****key and the three JAR paths as the value. Tanwi to check with team for screenshot from a live Dataproc console. This is the most important screenshot on the page — the****&#x20;****`spark.jars`****&#x20;****\*\*\*\*Properties field is not obvious to find and a screenshot here prevents the most common setup error.**_
 
 #### gcloud CLI
 
@@ -74,7 +72,7 @@ gcloud dataproc clusters create zingg-cluster \
   --optional-components=JUPYTER \
   --enable-component-gateway \
   --properties="^#^spark:spark.jars=\
-gs://$BUCKET/zingg-0.6.0.jar,\
+gs://$BUCKET/zingg-0.7.0.jar,\
 gs://$BUCKET/spark-3.5-bigquery-0.44.1.jar,\
 gs://$BUCKET/gcs-connector-hadoop3-latest.jar"
 ```
@@ -93,7 +91,6 @@ Once your cluster status shows **Running**, access the managed JupyterLab enviro
 4. Under **Component Gateway**, click the **JupyterLab** link.
 5. Create a new notebook and select the **PySpark** kernel.
 
-_**IMAGE TO BE ADDED— Dataproc cluster Web Interfaces tab showing the Component Gateway section with the JupyterLab link highlighted. Tanwi to check with team for screenshot from a live Dataproc cluster.**_
 
 ### Step 4: Set a checkpoint directory and install Zingg
 
@@ -200,7 +197,6 @@ spark_df = spark_df.toDF(*schema_list)
 spark_df.limit(10).toPandas().head()
 ```
 
-_**IMAGE TO BE ADDED — Jupyter notebook cell showing the preview output table with sample FEBRL data — the same customer appearing multiple times with field variations across rows. Source: not in the GCP docx (text-only guide). Tanwi to screenshot from a live notebook run. Same principle as the Databricks guide — this image shows readers the exact problem Zingg is solving before they configure anything. Place: below the\*\*\*\*****&#x20;****`spark_df.limit(10).toPandas().head()`****&#x20;****\*\*\*\*line.**_
 
 ### Step 8: Configure input and output pipes
 
@@ -353,7 +349,6 @@ display(widgets.VBox(children=vContainers))
 ready_for_save = True
 ```
 
-_**IMAGE TO BE ADDED — Zingg labeling widget rendered in JupyterLab on Dataproc, showing two candidate records side by side with Match / No Match / Uncertain toggle buttons. Tanwi to check with team for screenshot from a live Dataproc notebook run.**_
 
 {% hint style="success" icon="right-long" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 11–14 in a loop until you reach this target. Label until all field types and data variation patterns in your schema are covered. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are underrepresented.
@@ -411,7 +406,6 @@ with open(DOCS_DIR + "data.html", 'r') as f:
     display(HTML(f.read()))
 ```
 
-_**IMAGE TO BE ADDED—****&#x20;****`generateDocs`****\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* \*\*\*\*HTML output rendered inside JupyterLab on Dataproc showing labeled pair examples. Source: not in the GCP docx. Tanwi to check with team for screenshot from a live notebook run.**_
 
 {% hint style="success" icon="right-long" %}
 `generateDocs` is optional. Skip it if you have 30–40 matches and 30–40 non-matches and are confident in your labeling quality.
@@ -461,7 +455,6 @@ final_results = outputDF.toDF(*colNames)
 final_results.show(10)
 ```
 
-_**IMAGE TO BE ADDED — match output table in JupyterLab on Dataproc showing resolved records with\*\*\*\*****&#x20;****`z_cluster`****&#x20;****column visible. Highlight two rows sharing the same****&#x20;****`z_cluster`****&#x20;****\*\*\*\*value to show they have been resolved to the same entity.Tanwi to check with team for screenshot from a live notebook run.**_
 
 {% hint style="success" icon="right-long" %}
 * `z_cluster`— unique entity ID assigned by Zingg. All records sharing the same `z_cluster` represent the same real-world entity. Group by `z_cluster` to collapse duplicates into a golden record.
@@ -473,6 +466,159 @@ For threshold guidance and full output column definitions → [Interpret Output
 {% endtab %}
 
 {% tab title="Enterprise" %}
+{% hint style="info" icon="right-long" %}
+Enterprise requires a Zingg licence and the Enterprise GCP package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
+{% endhint %}
+
+Uses `EArguments`, `EFieldDefinition`, `ECsvPipe`, `EZinggWithSpark`. Adds blocking model configuration, primary key for incremental matching, stats output, deterministic matching rules, pass-through expressions, the `runIncremental` phase, and the `explain` phase.
+
+### Prerequisites
+
+* GCP account with Dataproc and GCS access
+* Zingg Enterprise license
+* Enterprise JAR: `zingg-enterprise-spark-0.7.0.jar`
+* Enterprise Python packages: `zinggEC`, `zinggES`
+
+### Step 1: Prepare GCS bucket and upload Enterprise artifacts
+
+Create a GCS bucket and upload the Enterprise JAR and Python wheels:
+
+```bash
+BUCKET="zingg-enterprise-storage"
+
+gcloud storage buckets create gs://$BUCKET --location=us-central1
+
+gsutil cp zingg-enterprise-spark-0.7.0.jar gs://$BUCKET/jars/
+gsutil cp zinggEC-0.7.0-py3-none-any.whl gs://$BUCKET/wheels/
+gsutil cp zinggES-0.7.0-py3-none-any.whl gs://$BUCKET/wheels/
+gsutil cp your-data.csv gs://$BUCKET/data/
+```
+
+### Step 2: Create a Dataproc cluster with Enterprise JAR
+
+```bash
+gcloud dataproc clusters create zingg-enterprise-cluster \
+  --region=us-central1 \
+  --image-version=2.2-debian12 \
+  --master-machine-type=n2-standard-4 \
+  --worker-machine-type=n2-standard-4 \
+  --num-workers=4 \
+  --optional-components=JUPYTER \
+  --enable-component-gateway \
+  --properties="^#^spark:spark.jars=\
+gs://$BUCKET/jars/zingg-enterprise-spark-0.7.0.jar"
+```
+
+### Step 3: Install Enterprise Python packages
+
+In JupyterLab on the cluster:
+
+```python
+!pip install gs://$BUCKET/wheels/zinggEC-0.7.0-py3-none-any.whl
+!pip install gs://$BUCKET/wheels/zinggES-0.7.0-py3-none-any.whl
+!pip install zingg==0.7.0
+
+# Verify installation
+!pip show zinggEC
+!pip show zinggES
+```
+
+### Step 4: Configure and run Zingg Enterprise
+
+Set up checkpoint directory and import libraries:
+
+```python
+checkpoint_path = f"gs://{BUCKET}/zingg_checkpoint"
+spark.sparkContext.setCheckpointDir(checkpoint_path)
+
+from zinggEC.enterprise.common.EArguments import *
+from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition
+from zinggEC.enterprise.common.epipes import *
+from zinggES.enterprise.spark.ESparkClient import *
+from zingg.client import *
+from zingg.pipes import *
+import pandas as pd
+import numpy as np
+```
+
+Configure Enterprise arguments:
+
+```python
+BUCKET = "your-bucket-name"
+modelId = "testModelFebrl"
+zinggDir = f"gs://{BUCKET}/models"
+
+args = EArguments()
+args.setModelId(modelId)
+args.setZinggDir(zinggDir)
+args.setBlockingModel("DEFAULT")  # Use "WIDER" if matches are being missed
+args.setNumPartitions(16)
+args.setLabelDataSampleSize(0.5)
+```
+
+Configure input/output pipes and field definitions (similar to Community but using Enterprise classes):
+
+```python
+inputPipe = ECsvPipe("testFebrl", f"gs://{BUCKET}/test.csv", schema)
+args.setData(inputPipe)
+
+outputPipe = ECsvPipe("resultOutput", f"gs://{BUCKET}/results")
+args.setOutput(outputPipe)
+
+# Field definitions using EFieldDefinition
+fieldDefs = [
+  EFieldDefinition("id", "string", MatchType.EXACT),
+  EFieldDefinition("fname", "string", MatchType.FUZZY),
+  EFieldDefinition("lname", "string", MatchType.FUZZY),
+  # ... add all your fields
+]
+args.setFieldDefinition(fieldDefs)
+```
+
+Run Enterprise phases:
+
+```python
+# Find training data
+options = ClientOptions([ClientOptions.PHASE, "findTrainingData"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+
+# Label (interactive widget)
+options = ClientOptions([ClientOptions.PHASE, "label"])
+zingg = EZinggWithSpark(args, options)
+zingg.init()
+
+# Train
+options = ClientOptions([ClientOptions.PHASE, "train"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+
+# Match
+options = ClientOptions([ClientOptions.PHASE, "match"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+
+# Enterprise-only: explain results
+options = ClientOptions([ClientOptions.PHASE, "explain"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+
+# Enterprise-only: incremental matching
+options = ClientOptions([ClientOptions.PHASE, "runIncremental"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+```
+
+### Key Differences from Community Edition
+
+| Feature | Community | Enterprise |
+|---------|-----------|------------|
+| Classes | `Arguments`, `FieldDefinition`, `CsvPipe`, `ZinggWithSpark` | `EArguments`, `EFieldDefinition`, `ECsvPipe`, `EZinggWithSpark` |
+| Blocking | Default only | `DEFAULT` or `WIDER` strategy |
+| Incremental | Not available | `runIncremental` phase |
+| Explainability | Not available | `explain` phase |
+| Deterministic | Not available | Built-in deterministic rules |
+| Pass-through | Not available | Pass-through expressions |
 
 {% endtab %}
 {% endtabs %}
diff --git a/docs/platform-guides/platform-guide-for-microsoft-fabric.md b/docs/platform-guides/platform-guide-for-microsoft-fabric.md
index 251ebd19c..b4e6effd7 100644
--- a/docs/platform-guides/platform-guide-for-microsoft-fabric.md
+++ b/docs/platform-guides/platform-guide-for-microsoft-fabric.md
@@ -480,6 +480,327 @@ print(outputDF.count())
 {% endtab %}
 
 {% tab title="Enterprise" %}
+{% hint style="info" icon="right-long" %}
+Enterprise requires a Zingg licence and the Enterprise Fabric package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
+{% endhint %}
+
+Uses `EArguments`, `EFieldDefinition`, `ECsvPipe`, `EZinggWithSpark`. Seven notebooks, each covering one phase. Enterprise adds blocking model configuration, a primary key for incremental matching, stats output, deterministic matching rules, pass-through expressions, the `runIncremental` phase, and the `explain` phase.
+
+### Prerequisites
+
+* Microsoft Fabric workspace with Lakehouse
+* Zingg Enterprise license
+* Enterprise JAR: `zingg-enterprise-spark-0.7.0.jar`
+* Enterprise Python packages: `zinggEC`, `zinggES`
+* License JAR: `zingg_license.jar`
+
+### Fabric workspace setup (Enterprise)
+
+Complete these steps in the Fabric UI before opening any notebook.
+
+#### **Step 1: Create a Fabric workspace**
+
+Same as Community - create a workspace named `Zingg-Fabric-Enterprise`.
+
+#### **Step 2: Create a Zingg Enterprise Environment**
+
+1. Inside your workspace, go to the **Environment** tab and click **New Environment**.
+2. Name it `Zingg Enterprise Environment`.
+
+#### **Step 3: Install Enterprise JARs in the Environment**
+
+1. Download the Enterprise package (provided with your license) which includes:
+   - `zingg-enterprise-spark-0.7.0.jar`
+   - `zingg_license.jar`
+2. Open your `Zingg Enterprise Environment`, go to **Custom Library**, and upload both JAR files.
+3. Click **Save** and then **Publish** the Environment.
+
+#### **Step 4: Create a Lakehouse and upload your data**
+
+Same as Community - create a Lakehouse and upload your CSV file to OneLake.
+
+### Notebook 01: Set up Zingg Enterprise
+
+Create a new notebook in your workspace, attach it to the `Zingg Enterprise Environment`, and select **PySpark** as the kernel.
+
+#### **Step 5: Verify Spark is configured correctly**
+
+```python
+spark.sparkContext.getConf().get('spark.hadoop.trident.workspace.id')
+```
+
+#### **Step 6: Set the checkpoint directory**
+
+```python
+spark.sparkContext.setCheckpointDir("Files")
+```
+
+#### **Step 7: Install Enterprise Python packages**
+
+```python
+pip install zingg
+pip install zinggEC
+pip install zinggES
+```
+
+Verify installation:
+
+```python
+!pip show zingg
+!pip show zinggEC
+!pip show zinggES
+```
+
+{% hint style="info" icon="right-long" %}
+All three must show as installed: `zingg` (Community base), `zinggEC` (Enterprise), `zinggES` (Enterprise Plus). If any show as not found, install the corresponding `.whl` file from the cluster Libraries tab using the wheels provided in your Enterprise package.
+{% endhint %}
+
+#### **Step 8: Set the model ID and storage paths**
+
+Replace `<workspace-id>` and `<lakehouse-id>` with the actual IDs from your Fabric workspace and Lakehouse.
+
+```python
+files_dir = (
+    "abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/"
+    "<lakehouse-id>/Files"
+)
+zingg_dir = files_dir + "/zingg"
+model_id = "zinggModel"
+MARKED_DIR = zingg_dir + "/" + model_id + "/trainingData/marked/"
+UNMARKED_DIR = zingg_dir + "/" + model_id + "/trainingData/unmarked/"
+```
+
+#### **Step 9: Import libraries**
+
+```python
+import pandas as pd
+import numpy as np
+import os, time, uuid
+from ipywidgets import widgets, interact, GridspecLayout
+import base64
+import pyspark.sql.functions as fn
+from zinggEC.enterprise.common.ApproverArguments import *
+from zinggEC.enterprise.common.IncrementalArguments import *
+from zinggEC.enterprise.common.epipes import *
+from zinggEC.enterprise.common.EArguments import *
+from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition
+from zinggES.enterprise.spark.ESparkClient import *
+from zingg.client import *
+from zingg.pipes import *
+```
+
+#### **Step 10: Build the Enterprise arguments object**
+
+`EArguments` is the Enterprise equivalent of `Arguments`. `setBlockingModel` sets the blocking strategy. `DEFAULT` suits most datasets—use `WIDER` if you know matching pairs are being missed.
+
+```python
+args = EArguments()
+args.setModelId(model_id)
+args.setZinggDir(zingg_dir)
+args.setBlockingModel("DEFAULT")
+```
+
+#### **Step 11: Configure performance settings**
+
+```python
+args.setNumPartitions(32)
+spark.conf.set("spark.sql.adaptive.enabled", False)
+```
+
+{% hint style="danger" icon="right-long" %}
+Set `numPartitions` to approximately 20–30× your worker vCPU count. `labelDataSampleSize` is set in Notebook 03 where the labeling loop runs.
+{% endhint %}
+
+#### **Step 12: Preview your data**
+
+Same as Community Notebook 01, Step 11.
+
+#### **Step 13: Configure input and output pipes**
+
+Use `ECsvPipe` for Enterprise:
+
+```python
+schema = (
+    "rec_id string, fname string, "
+    "lname string, stNo string, "
+    "add1 string, add2 string, "
+    "city string, areacode string, "
+    "state string, dob string, "
+    "ssn string"
+)
+
+inputPipe = ECsvPipe(
+    "inputpipe",
+    "abfss://<workspace-id>@onelake"
+    ".dfs.fabric.microsoft.com/"
+    "<lakehouse-id>/Files/test.csv",
+    schema
+)
+args.setData(inputPipe)
+
+output_path = (
+    "abfss://<workspace-id>@onelake"
+    ".dfs.fabric.microsoft.com/"
+    "<lakehouse-id>/Files/Output" + model_id
+)
+outputPipe = ECsvPipe("resultOutput", output_path)
+args.setOutput(outputPipe)
+```
+
+#### **Step 14: Define fields and match types**
+
+Use `EFieldDefinition` for Enterprise:
+
+```python
+rec_id = EFieldDefinition("rec_id", "string", MatchType.DONT_USE)
+fname = EFieldDefinition("fname", "string", MatchType.FUZZY)
+lname = EFieldDefinition("lname", "string", MatchType.FUZZY)
+stNo = EFieldDefinition("stNo", "string", MatchType.FUZZY)
+add1 = EFieldDefinition("add1", "string", MatchType.FUZZY)
+add2 = EFieldDefinition("add2", "string", MatchType.FUZZY)
+city = EFieldDefinition("city", "string", MatchType.FUZZY)
+areacode = EFieldDefinition("areacode", "string", MatchType.FUZZY)
+state = EFieldDefinition("state", "string", MatchType.FUZZY)
+dob = EFieldDefinition("dob", "string", MatchType.EXACT)
+ssn = EFieldDefinition("ssn", "string", MatchType.EXACT)
+
+fieldDefs = [
+    rec_id, fname, lname, stNo, add1,
+    add2, city, areacode, state, dob,
+    ssn
+]
+args.setFieldDefinition(fieldDefs)
+```
+
+### Notebook 02: Find training data
+
+This notebook runs `findTrainingData`. It calls `%run 01-setting_up_zingg` at the top.
+
+#### **Step 15: Set `labelDataSampleSize`**
+
+```python
+args.setNumPartitions(4)
+args.setLabelDataSampleSize(0.4)
+```
+
+#### **Step 16: Find candidate pairs**
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "findTrainingData"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+```
+
+### Notebook 03: Label pairs
+
+This notebook runs `label`. It calls `%run 01-setting_up_zingg` at the top.
+
+#### **Step 17: Load pairs for labeling**
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "label"])
+zingg = EZinggWithSpark(args, options)
+zingg.init()
+
+candidate_pairs_pd = getPandasDfFromDs(zingg.getUnmarkedRecords())
+
+if candidate_pairs_pd.shape[0] == 0:
+    print("No pairs found. Run findTrainingData first.")
+else:
+    z_clusters = list(np.unique(candidate_pairs_pd['z_cluster']))
+    print(f"{len(z_clusters)} candidate pairs found for labeling")
+```
+
+#### **Step 18: Label pairs in the widget**
+
+Same widget code as Community (Step 17).
+
+#### **Step 19: Save labeled pairs**
+
+```python
+if not ready_for_save:
+    print("Run the widget cell first.")
+else:
+    for pair in vContainers[1:]:
+        user_assigned_label = pair.children[1].get_interact_value()
+        start = pair.children[0].value.find('data-title="')
+        if start > 0:
+            start += len('data-title="')
+            end = pair.children[0].value.find('"', start+2)
+        pair_id = pair.children[0].value[start:end]
+        candidate_pairs_pd.loc[
+            candidate_pairs_pd['z_cluster'] == pair_id,
+            'z_isMatch'] = LABELS.get(user_assigned_label)
+
+    notebookutils.fs.mkdirs(MARKED_DIR)
+    zingg.writeLabelledOutputFromPandas(
+        candidate_pairs_pd, args)
+
+    marked_pd_df = getPandasDfFromDs(
+        zingg.getMarkedRecords())
+    n_pos, n_neg, n_uncer, n_tot = \
+        count_labeled_pairs(marked_pd_df)
+    print(f"Out of total {n_tot} pairs,")
+    print(f"You have accumulated {n_pos} pairs labeled as positive matches.")
+    print(f"You have accumulated {n_neg} pairs labeled as not matches.")
+    print(f"You have accumulated {n_uncer} pairs labeled as uncertain.")
+    print("Run Steps 16-19 again if you need more pairs.")
+    ready_for_save = False
+```
+
+### Notebook 04: Generate model documentation (optional)
+
+Run `generateDocs` after labeling to produce HTML reports of your training data.
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "generateDocs"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+
+DOCS_DIR = zingg_dir + "/" + model_id + "/docs/"
+displayHTML(open(DOCS_DIR + "model.html", 'r').read())
+displayHTML(open(DOCS_DIR + "data.html", 'r').read())
+```
+
+### Notebook 05: Train and match
+
+#### **Step 20: Train and match**
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "trainMatch"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+```
+
+Run `train` and `match` separately if you want to inspect the model first.
+
+### Notebook 06: Explain results (Enterprise only)
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "explain"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+```
+
+### Notebook 07: Incremental matching (Enterprise only)
+
+```python
+options = ClientOptions([ClientOptions.PHASE, "runIncremental"])
+zingg = EZinggWithSpark(args, options)
+zingg.initAndExecute()
+```
+
+### Key Differences from Community Edition
+
+| Feature | Community | Enterprise |
+|---------|-----------|------------|
+| Classes | `Arguments`, `FieldDefinition`, `CsvPipe`, `ZinggWithSpark` | `EArguments`, `EFieldDefinition`, `ECsvPipe`, `EZinggWithSpark` |
+| Blocking | Default only | `DEFAULT` or `WIDER` strategy |
+| Incremental | Not available | `runIncremental` phase |
+| Explainability | Not available | `explain` phase |
+| Deterministic | Not available | Built-in deterministic rules |
+| Pass-through | Not available | Pass-through expressions |
+| Notebooks | 4 | 7 |
 
 {% endtab %}
 {% endtabs %}
diff --git a/docs/platform-guides/platform-guide-for-snowflake.md b/docs/platform-guides/platform-guide-for-snowflake.md
index 8197dc3a7..a5871fbb6 100644
--- a/docs/platform-guides/platform-guide-for-snowflake.md
+++ b/docs/platform-guides/platform-guide-for-snowflake.md
@@ -39,8 +39,8 @@ Host <IP address of EC2 instance>
 3. Click **Open a Remote Window** in VS Code, select your EC2 host, and connect. 4. Pull the Zingg Docker image and start a bash session inside the container:
 
 ```bash
-docker pull zingg/zingg:0.6.0
-docker run -it zingg/zingg:0.6.0 bash
+docker pull zingg/zingg:0.7.0
+docker run -it zingg/zingg:0.7.0 bash
 ```
 
 Inside the container, your container ID is the alphanumeric string between `@` and `:` in the terminal prompt. For example in `root@fab997383957:/zingg#`, the container ID is `fab997383957`. Note this value—you will need it in Step 3.
@@ -210,8 +210,6 @@ When the `.pem` key file is included in the zip, Lambda extracts it to `/var/tas
 A successful test returns `HTTP 200` and `Started phase: findTrainingData`. Monitor progress via `tail -f logfile.txt` on EC2.
 {% endhint %}
 
-_**IMAGE TO BE ADDED — AWS Lambda function test screen showing a successful HTTP 200 response with the "Started phase: findTrainingData" body. Tanwi to check with team for screenshot this from a live Lambda test run and add here. Caption: "Lambda function test returning HTTP 200 — Zingg phase started successfully."**_
-
 ### **Step 5: Connect Lambda to Snowflake via external function**
 
 1. Create a new IAM role. Entity type: **Another AWS account**. Specify your AWS Account ID.
@@ -242,7 +240,7 @@ SELECT run_zingg('findTrainingData');
 
 **Run label interactively on EC2 directly**
 
-```sql
+```bash
 ./zingg.sh --phase label --conf examples/febrl/configSnow.json
 ```
 
@@ -284,7 +282,7 @@ tail -f nohup.out
 
 Or from Snowflake:
 
-```bash
+```sql
 SELECT run_zingg('checklog');
 ```
 
@@ -345,6 +343,160 @@ Enterprise only. Zingg Enterprise runs natively inside Snowflake using Snowpark.
 Enterprise requires a Zingg licence and the Enterprise Snowflake package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
-**Content for the Enterprise Snowflake platform guide is being prepared. This section will be updated with full step-by-step instructions once confirmed by the team.**
+### **Prerequisites**
+
+* Snowflake account with Snowpark enabled
+* Zingg Enterprise license
+* Python 3.8+ with Snowpark session
+* Access to Zingg Enterprise Snowflake package
+
+### **Step 1: Set up Snowpark Session**
+
+Install the Snowpark Python package and Zingg Enterprise package:
+
+```bash
+pip install snowflake-snowpark-python
+# Zingg Enterprise package provided after license purchase
+pip install zingg-enterprise-snowflake
+```
+
+Create a Snowpark session:
+
+```python
+from snowflake.snowpark import Session
+
+connection_parameters = {
+    "account": "<your-account>",
+    "user": "<your-username>",
+    "password": "<your-password>",
+    "role": "<your-role>",
+    "warehouse": "<your-warehouse>",
+    "database": "<your-database>",
+    "schema": "<your-schema>"
+}
+
+session = Session.builder.configs(connection_parameters).create()
+```
+
+### **Step 2: Configure Zingg Enterprise**
+
+Create a configuration dictionary for Zingg Enterprise:
+
+```python
+from zingg_enterprise import ZinggEnterprise
+
+config = {
+    "data": [{
+        "name": "inputData",
+        "table": "your_input_table",
+        "database": "your_database",
+        "schema": "your_schema"
+    }],
+    "output": [{
+        "name": "outputData",
+        "table": "your_output_table",
+        "database": "your_database",
+        "schema": "your_schema"
+    }],
+    "modelId": "100",
+    "zinggDir": "@your_stage/zingg_models",
+    "numPartitions": 4,
+    "labelDataSampleSize": 0.5,
+    "fieldDefinition": [
+        {
+            "fieldName": "fname",
+            "matchType": "FUZZY",
+            "fields": "fname",
+            "dataType": "string"
+        },
+        {
+            "fieldName": "lname",
+            "matchType": "FUZZY",
+            "fields": "lname",
+            "dataType": "string"
+        }
+    ]
+}
+
+zingg = ZinggEnterprise(session, config)
+```
+
+### **Step 3: Run Zingg Phases**
+
+Run phases directly from Python/Snowflake - no external infrastructure needed:
+
+```python
+# Find training data
+zingg.find_training_data()
+
+# Label interactively (opens labeling UI in Snowsight or Jupyter)
+zingg.label()
+
+# Train the model
+zingg.train()
+
+# Run matching
+results = zingg.match()
+
+# View results
+results.show()
+
+# Check statistics
+stats = zingg.get_statistics()
+print(stats)
+```
+
+### **Step 4: Incremental Matching**
+
+For incremental loads:
+
+```python
+# Configure incremental run
+incremental_config = {
+    **config,
+    "incremental": True,
+    "lastRunTimestamp": "2024-01-15 00:00:00"
+}
+
+zingg_incremental = ZinggEnterprise(session, incremental_config)
+results = zingg_incremental.match()
+```
+
+### **Key Differences from Community Edition**
+
+| Feature | Community | Enterprise |
+|---------|-----------|------------|
+| Infrastructure | EC2 + Docker + Lambda | Native Snowpark |
+| Deployment | Manual setup | Snowflake Marketplace / Package |
+| Scaling | EC2 instance sizing | Snowflake warehouse scaling |
+| Execution | Spark on EC2 | Snowpark on Snowflake |
+| Monitoring | Lambda logs + EC2 | Snowflake Query History |
+| Incremental | Via Lambda | Native API |
+
+### **Snowflake Marketplace Deployment (Recommended)**
+
+1. Search for "Zingg Enterprise" in Snowflake Marketplace
+2. Click **Get** to install in your Snowflake account
+3. Follow the setup wizard to configure database, schema, and warehouse
+4. Access Zingg functions directly from SQL:
+
+```sql
+-- Run Zingg phases from SQL
+CALL ZINGG.FIND_TRAINING_DATA();
+CALL ZINGG.LABEL();
+CALL ZINGG.TRAIN();
+CALL ZINGG.MATCH();
+```
+
+### **Getting Enterprise Access**
+
+Zingg Enterprise for Snowflake requires a commercial license. To get started:
+
+1. Visit [Zingg Enterprise Snowflake](https://www.zingg.ai/company/zingg-enterprise-snowflake)
+2. Contact sales for a trial license
+3. Receive the Enterprise package and installation instructions
+4. Follow this guide for setup
+
+For detailed API reference, see the [Zingg Python API Documentation](../zingg-python-api/enterprise-zingges-python-api.md).
 {% endtab %}
-{% endtabs %}
+{% endtabs %}
\ No newline at end of file
diff --git a/docs/pretrainedModels.md b/docs/pretrainedModels.md
index 88c5ca2e8..3a1ad9ee9 100644
--- a/docs/pretrainedModels.md
+++ b/docs/pretrainedModels.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Pre-trained Zingg models for common datasets including Febrl, North Carolina Voters, and Amazon-Google products.
 nav_order: 6
 parent: Zingg Models
 ---
diff --git a/docs/pythonEC/markdown/ApproverArguments.md b/docs/pythonEC/markdown/ApproverArguments.md
index 8d759e8a6..015a569bb 100644
--- a/docs/pythonEC/markdown/ApproverArguments.md
+++ b/docs/pythonEC/markdown/ApproverArguments.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  ApproverArguments class reference for Zingg Enterprise cluster approval. Configure approval queries, destination output, and parent EArguments for manual cluster review workflow.
+---
+
 # ApproverArguments
 
 ## zinggEC.enterprise.common.ApproverArguments
diff --git a/docs/pythonEC/markdown/EArguments.md b/docs/pythonEC/markdown/EArguments.md
index b844e6f9b..b2f7e50c8 100644
--- a/docs/pythonEC/markdown/EArguments.md
+++ b/docs/pythonEC/markdown/EArguments.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  EArguments class reference for Zingg Enterprise. Extends Arguments with blocking model, deterministic matching, primary key, output stats, and passthrough expression.
+---
+
 # EArguments
 
 ## zinggEC.enterprise.common.EArguments
diff --git a/docs/pythonEC/markdown/EFieldDefinition.md b/docs/pythonEC/markdown/EFieldDefinition.md
index fe752f496..b3f8256ad 100644
--- a/docs/pythonEC/markdown/EFieldDefinition.md
+++ b/docs/pythonEC/markdown/EFieldDefinition.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  EFieldDefinition class reference for Zingg Enterprise. Extends FieldDefinition with primary key support and post-processors for output value standardization.
+---
+
 # EFieldDefinition
 
 ## zinggEC.enterprise.common.EFieldDefinition
diff --git a/docs/pythonEC/markdown/IncrementalArguments.md b/docs/pythonEC/markdown/IncrementalArguments.md
index cd90cf494..0f9b94d18 100644
--- a/docs/pythonEC/markdown/IncrementalArguments.md
+++ b/docs/pythonEC/markdown/IncrementalArguments.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  IncrementalArguments class reference for Zingg Enterprise. Configure incremental data, delete actions, output temp path, and parent EArguments.
+---
+
 # IncrementalArguments
 
 ## zinggEC.enterprise.common.IncrementalArguments
diff --git a/docs/pythonEC/markdown/MappingMatchType.md b/docs/pythonEC/markdown/MappingMatchType.md
index 8cd742084..04b5ed1ca 100644
--- a/docs/pythonEC/markdown/MappingMatchType.md
+++ b/docs/pythonEC/markdown/MappingMatchType.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  MappingMatchType class reference for Zingg Enterprise. Use domain expertise (nicknames, company abbreviations) to enhance matching accuracy via JSON mapping files.
+---
+
 # MappingMatchType
 
 ## zinggEC.enterprise.common.MappingMatchType
diff --git a/docs/pythonEC/markdown/epipes.md b/docs/pythonEC/markdown/epipes.md
index 79cba520e..379f8577d 100644
--- a/docs/pythonEC/markdown/epipes.md
+++ b/docs/pythonEC/markdown/epipes.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Enterprise pipes module reference. Includes ECsvPipe for CSV files, EPipe base class with passthrough support, InMemoryPipe, and UCPipe for Unity Catalog Delta tables.
+---
+
 # epipes
 
 ## zinggEC.enterprise.common.epipes
diff --git a/docs/pythonEC/markdown/index.md b/docs/pythonEC/markdown/index.md
index f2bcf2851..7a4eccd56 100644
--- a/docs/pythonEC/markdown/index.md
+++ b/docs/pythonEC/markdown/index.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg Enterprise Entity Resolution Python Package reference. Complete API documentation for EC, ES packages including EArguments, EFieldDefinition, epipes, IncrementalArguments, and example usage.
+---
+
 <!-- Zingg Enterprise documentation master file, created by
 sphinx-quickstart on Wed Jul 16 14:57:19 2025.
 You can adapt this file completely to your liking, but it should at least
diff --git a/docs/pythonEC/markdown/zinggEC.md b/docs/pythonEC/markdown/zinggEC.md
index 564feaf3a..bee2e74c4 100644
--- a/docs/pythonEC/markdown/zinggEC.md
+++ b/docs/pythonEC/markdown/zinggEC.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg Enterprise Common (EC) Python package reference. Includes ApproverArguments, IncrementalArguments, MappingMatchType, epipes, EArguments, and EFieldDefinition classes.
+---
+
 # Zingg Enterprise Common Package
 
 Zingg Enterprise Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
diff --git a/docs/pythonES/markdown/ESparkClient.md b/docs/pythonES/markdown/ESparkClient.md
index d0c27632c..8336ade6d 100644
--- a/docs/pythonES/markdown/ESparkClient.md
+++ b/docs/pythonES/markdown/ESparkClient.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  ESparkClient class reference for Zingg Enterprise Snowflake. Main entry point for ES Python API. Initialize with EArguments and ClientOptions, run init(), execute(), executeLabel(), and write labelled output.
+---
+
 # ESparkClient
 
 ## zinggES.enterprise.spark.ESparkClient
diff --git a/docs/pythonES/markdown/index.md b/docs/pythonES/markdown/index.md
index efcb69fee..f6d48b035 100644
--- a/docs/pythonES/markdown/index.md
+++ b/docs/pythonES/markdown/index.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg Enterprise Entity Resolution Python Package reference. Complete API documentation for ES package including ESparkClient and zinggES classes with example usage.
+---
+
 <!-- Zingg Enterprise documentation master file, created by
 sphinx-quickstart on Wed Jul 16 19:15:34 2025.
 You can adapt this file completely to your liking, but it should at least
diff --git a/docs/pythonES/markdown/zinggES.md b/docs/pythonES/markdown/zinggES.md
index 882f0348b..9564f79ea 100644
--- a/docs/pythonES/markdown/zinggES.md
+++ b/docs/pythonES/markdown/zinggES.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg Enterprise Spark (ES) Python package reference. Includes ESparkClient for Snowflake-based entity resolution with ML.
+---
+
 # Zingg Enterprise Spark Package
 
 Zingg Enterprise Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.client.Arguments.md b/docs/pythonOss/markdown/_autosummary/zingg.client.Arguments.md
index 748dbec01..0b6563af9 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.client.Arguments.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.client.Arguments.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Arguments class reference for Community edition. Configure model ID, Zingg directory, partitions, label sample size, field definitions, data pipes, output pipes, stopwords cutoff, and training samples.
+---
+
 # zingg.client.Arguments
 
 ### *class* zingg.client.Arguments
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.client.ClientOptions.md b/docs/pythonOss/markdown/_autosummary/zingg.client.ClientOptions.md
index 0f4ceb5cf..83583a163 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.client.ClientOptions.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.client.ClientOptions.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  ClientOptions class reference for Community edition. Configure Zingg phase (trainMatch, train, match, link, findTrainingData, recommend), conf, location, and other options.
+---
+
 # zingg.client.ClientOptions
 
 ### *class* zingg.client.ClientOptions(argsSent=None)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.client.FieldDefinition.md b/docs/pythonOss/markdown/_autosummary/zingg.client.FieldDefinition.md
index 9a17cabc2..f43347f71 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.client.FieldDefinition.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.client.FieldDefinition.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  FieldDefinition class reference for Community edition. Define field name, data type, match type (FUZZY, EXACT, etc.), and optional stopwords for matching configuration.
+---
+
 # zingg.client.FieldDefinition
 
 ### *class* zingg.client.FieldDefinition(name, dataType, \*matchType)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.client.Zingg.md b/docs/pythonOss/markdown/_autosummary/zingg.client.Zingg.md
index e03b461cc..27321f5ac 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.client.Zingg.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.client.Zingg.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Zingg class reference for Community edition. Main interface for entity resolution. Initialize with Arguments and ClientOptions, then run init(), execute(), executeLabel(), and write labelled output.
+---
+
 # zingg.client.Zingg
 
 ### *class* zingg.client.Zingg(args, options)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.client.ZinggWithSpark.md b/docs/pythonOss/markdown/_autosummary/zingg.client.ZinggWithSpark.md
index f1d570637..e021a6a8b 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.client.ZinggWithSpark.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.client.ZinggWithSpark.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  ZinggWithSpark class reference for Community edition. Extends Zingg for Spark integration. Initialize with Arguments and ClientOptions, run init(), execute(), executeLabel(), and write labelled output.
+---
+
 # zingg.client.ZinggWithSpark
 
 ### *class* zingg.client.ZinggWithSpark(args, options)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.pipes.BigQueryPipe.md b/docs/pythonOss/markdown/_autosummary/zingg.pipes.BigQueryPipe.md
index 1dcb47227..e4a6e3bc2 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.pipes.BigQueryPipe.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.pipes.BigQueryPipe.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  BigQueryPipe class reference for Community edition. Configure BigQuery table, credential file, temporary GCS bucket, views enabled, and schema for data input/output.
+---
+
 # zingg.pipes.BigQueryPipe
 
 ### *class* zingg.pipes.BigQueryPipe(name)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.pipes.CsvPipe.md b/docs/pythonOss/markdown/_autosummary/zingg.pipes.CsvPipe.md
index c9ea56656..b12bc1f08 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.pipes.CsvPipe.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.pipes.CsvPipe.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  CsvPipe class reference for Community edition. Configure CSV file location, delimiter, header, and schema for data input/output.
+---
+
 # zingg.pipes.CsvPipe
 
 ### *class* zingg.pipes.CsvPipe(name, location=None, schema=None)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.pipes.Pipe.md b/docs/pythonOss/markdown/_autosummary/zingg.pipes.Pipe.md
index 912bc8b15..77614c6ee 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.pipes.Pipe.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.pipes.Pipe.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Pipe base class reference for Community edition. Configure pipe name, format (bigquery, csv, etc.), add properties, set schema, and retrieve pipe configuration.
+---
+
 # zingg.pipes.Pipe
 
 ### *class* zingg.pipes.Pipe(name, format)
diff --git a/docs/pythonOss/markdown/_autosummary/zingg.pipes.SnowflakePipe.md b/docs/pythonOss/markdown/_autosummary/zingg.pipes.SnowflakePipe.md
index 9b156f788..d45e475b9 100644
--- a/docs/pythonOss/markdown/_autosummary/zingg.pipes.SnowflakePipe.md
+++ b/docs/pythonOss/markdown/_autosummary/zingg.pipes.SnowflakePipe.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  SnowflakePipe class reference for Community edition. Configure Snowflake connection: URL, user, password, database, schema, warehouse, and table for data input/output.
+---
+
 # zingg.pipes.SnowflakePipe
 
 ### *class* zingg.pipes.SnowflakePipe(name)
diff --git a/docs/pythonOss/markdown/index.md b/docs/pythonOss/markdown/index.md
index 6adbd3b48..452a0cb30 100644
--- a/docs/pythonOss/markdown/index.md
+++ b/docs/pythonOss/markdown/index.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Community Zingg Entity Resolution Python Package reference. Complete API documentation for zingg.client and zingg.pipes modules with example usage.
+---
+
 <!-- zingg documentation master file, created by
 sphinx-quickstart on Thu Jul  7 12:24:41 2022.
 You can adapt this file completely to your liking, but it should at least
diff --git a/docs/pythonOss/markdown/zingg.client.md b/docs/pythonOss/markdown/zingg.client.md
index 8609efac7..758f01fea 100644
--- a/docs/pythonOss/markdown/zingg.client.md
+++ b/docs/pythonOss/markdown/zingg.client.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  zingg.client module reference for Community edition. Includes Zingg, ZinggWithSpark, Arguments, ClientOptions, and FieldDefinition classes for entity resolution.
+---
+
 # zingg.client module
 
 ## Classes
diff --git a/docs/pythonOss/markdown/zingg.pipes.md b/docs/pythonOss/markdown/zingg.pipes.md
index 22bf3a662..c56f63b8a 100644
--- a/docs/pythonOss/markdown/zingg.pipes.md
+++ b/docs/pythonOss/markdown/zingg.pipes.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  zingg.pipes module reference for Community edition. Includes Pipe base class, CsvPipe, BigQueryPipe, and SnowflakePipe for data source/sink connections.
+---
+
 # zingg.pipes module
 
 ## Classes
diff --git a/docs/reading.md b/docs/reading.md
index 5e872b5e1..d12377a10 100644
--- a/docs/reading.md
+++ b/docs/reading.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Curated reading list on entity resolution, identity resolution, customer data platforms, and master data management.
 nav_order: 11
 ---
 
diff --git a/docs/recipes-and-integration/combine-match-models.md b/docs/recipes-and-integration/combine-match-models.md
index e1d1b150b..161ddf9d7 100644
--- a/docs/recipes-and-integration/combine-match-models.md
+++ b/docs/recipes-and-integration/combine-match-models.md
@@ -127,7 +127,3 @@ The combination is defined in a JSON config file with two top-level arrays: `ver
 * `vertexType: 'zingg_match'` - the output of a full Zingg match model. The `config` field points to the model's config file.
 * `edgeType: 'same_edge'` - connects records that represent the same entity across vertices.
 * `strategy: 'pairs_and_vertices'` - the graph combination strategy. Determines how overlapping entity assignments are resolved.
-
-#### PYTHON API
-
-_**CHECK WITH SONAL \_ TEAM - Python API for combining match models is not documented in the reference PDF. Sonal to confirm before adding.**_
diff --git a/docs/reference/runtime-properties.md b/docs/reference/runtime-properties.md
index 6ea6b994d..de8365215 100644
--- a/docs/reference/runtime-properties.md
+++ b/docs/reference/runtime-properties.md
@@ -65,7 +65,7 @@ For GCP Dataproc, inject JARs via cluster creation properties.
 
 **Value**:
 
-* `gs://BUCKET/zingg-0.6.0.jar`
+* `gs://BUCKET/zingg-0.7.0.jar`
 * `gs://BUCKET/spark-3.5-bigquery-0.44.1.jar`
 * `gs://BUCKET/gcs-connector-hadoop3-latest.jar`
 
diff --git a/docs/running-zingg/cluster-approval.md b/docs/running-zingg/cluster-approval.md
index 960ffc3c1..b74fe5b1d 100644
--- a/docs/running-zingg/cluster-approval.md
+++ b/docs/running-zingg/cluster-approval.md
@@ -19,8 +19,6 @@ After Zingg runs the match or incremental phase, some clusters may need human re
 Approved and rejected decisions are stored by Zingg and preserved across subsequent incremental runs. Zingg will not override a human-approved or human-rejected cluster decision\
 in future runs, the human decision always takes precedence.
 
-_**CHECK WITH SONAL—The cluster approval page currently shows "Coming Soon" on the live docs with no technical content. All content above is written from KT notes and the incremental page context. Please review and confirm**_
-
 ### When to use cluster approval
 
 Records are high-stakes, and errors have downstream consequences. For example, a financial compliance system that links counterparties to sanctions lists or a KYC system where a false match could affect a customer's account.
diff --git a/docs/running-zingg/create-training-data/verify-blocking.md b/docs/running-zingg/create-training-data/verify-blocking.md
index 9b9e9f414..8b3b8c721 100644
--- a/docs/running-zingg/create-training-data/verify-blocking.md
+++ b/docs/running-zingg/create-training-data/verify-blocking.md
@@ -11,10 +11,7 @@ tags:
 
 The blocking model filters your dataset before any similarity comparisons happen. Records in different blocks are never compared. This is what makes Zingg scalable, but it also means that if the blocking model puts two matching records in different blocks, those records will never be matched no matter how well the similarity model is trained.
 
-`verifyBlocking` tells you what percentage of your known matching pairs are being blocked together correctly. Run this test after configuring Zingg and again after training if you suspect missed matches.\
-<br>
-
-TODO - move to verify blocking
+`verifyBlocking` tells you what percentage of your known matching pairs are being blocked together correctly. Run this test after configuring Zingg and again after training if you suspect missed matches.
 
 How to inspect the blocking model, understand its coverage, and fix missed matches caused by blocking.
 
@@ -95,7 +92,7 @@ influence matching.
 * [Label Training Pairs](label-training-pairs.md) - how to add more training data
 * [Configure Zingg](../configure-zingg.md) - changing field match types
 * [Blocking Strategies: DEFAULT vs WIDER](../../tuning/blocking-strategy.md) - reorder candidate fields in the blocking tree (Enterprise only)
-* [Custom Blocking and Similarity](/broken/pages/DWdyf7az3MmhJaVca3k7) Functions
+* [Custom Blocking and Similarity Functions](../../reference/custom-blocking-similarity.md) Functions
 {% endhint %}
 
 {% hint style="success" icon="right-long" %}
@@ -180,5 +177,5 @@ If `verifyBlocking` shows that many known matching pairs are not being blocked t
 **Read more**:
 
 * Blocking model concept and how it fits in the pipeline → [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
-* Custom blocking functions for advanced tuning → [Custom Blocking and Similarity](/broken/pages/DWdyf7az3MmhJaVca3k7)
+* Custom blocking functions for advanced tuning → [Custom Blocking and Similarity Functions](../../reference/custom-blocking-similarity.md)
 {% endhint %}
diff --git a/docs/running-zingg/experience-zingg.md b/docs/running-zingg/experience-zingg.md
index cb01d42e9..0fe9a97b0 100644
--- a/docs/running-zingg/experience-zingg.md
+++ b/docs/running-zingg/experience-zingg.md
@@ -10,8 +10,6 @@ description: >-
 **Prerequisites:** Docker installed on your machine. Nothing else required.
 {% endhint %}
 
-_**CHECK WITH SONAL - IF WE STILL NEED THIS PAGE AS PER THE NEW GROUPING - AS WE HAVE A DEDICATED PAGE FOR DOCKER - QUICK START. OUR INITIAL IDEA ABOUT KEEPING THIS PAGE IS NOW CHANGED.**_
-
 The fastest way to understand what Zingg does is to see it work. This page uses Docker and\
 a pre-trained model bundled with Zingg to run entity resolution on a sample dataset in three\
 commands. There is no installation, no configuration, and no labeling required.
@@ -70,10 +68,6 @@ The output contains all your input fields plus three columns added by Zingg:
 **Read more**: To know more about output scores, check [Interpret Output Score](../interpreting-results/interpret-output-scores.md).
 {% endhint %}
 
-### VIDEO TO BE ADDED
-
-**Paste the Zingg video URL here once available from the team.**
-
 <details>
 
 <summary><strong>What is the FEBRL dataset?</strong></summary>
diff --git a/docs/running-zingg/install-zingg.md b/docs/running-zingg/install-zingg.md
index 862fb69f7..6313de041 100644
--- a/docs/running-zingg/install-zingg.md
+++ b/docs/running-zingg/install-zingg.md
@@ -57,7 +57,7 @@ Restart the Python kernel: **Runtime → Restart Python**.
 {% endtab %}
 
 {% tab title="Microsoft Fabric" %}
-Zingg on Microsoft Fabric uses the Fabric notebook interface and a custom environment.\
+Zingg on Microsoft Fabric uses the Fabric notebook interface and a custom environment.
 Follow these steps before opening the Zingg notebook.
 
 ### **Step 1: Create a Fabric workspace and session**
@@ -170,15 +170,109 @@ Run Zingg Python code directly in AWS EMR Notebooks using the Python API. Instal
 {% endtab %}
 
 {% tab title="AWS Glue" %}
-**AWS Glue install steps are not documented on any live docs page. CHECK WITH SONAL before publishing this tab.**
+### **AWS Glue Studio Notebooks**
+
+Run Zingg Python code in AWS Glue Studio Notebooks using the Python API.
+
+#### Community
+
+```bash
+%pip install zingg
+%pip install tabulate
+%pip install ipywidgets
+```
+
+#### Enterprise Lite or Enterprise
+
+```bash
+%pip install zinggEC
+%pip install tabulate
+%pip install ipywidgets
+```
+
+#### Enterprise Plus
+
+```bash
+%pip install zinggES
+%pip install tabulate
+%pip install ipywidgets
+```
+
+### **AWS Glue Job (Spark)**
+
+For production jobs, use a Glue Job with the Zingg JAR and Python packages.
+
+1. Create a Glue Job with **Spark** as the job type.
+2. Under **Script filename**, upload your Zingg Python script.
+3. Under **Job parameters**, add:
+   - `--extra-jars`: `s3://your-bucket/jars/zingg-0.7.0.jar` (and all required Zingg JARs for Enterprise)
+   - `--additional-python-modules`: `zingg==0.7.0,tabulate,ipywidgets`
+4. Set **Worker type** to `G.1X` or `G.2X` and **Number of workers** based on data size.
+
+{% hint style="success" icon="right-long" %}
+For the full AWS Glue guide including IAM roles, S3 setup, and screenshots → [Platform Guide for AWS Glue](../platform-guides/platform-guide-for-aws-glue.md)
+{% endhint %}
 {% endtab %}
 
 {% tab title="GCP Dataproc" %}
-_**CHECK WITH SONAL - - NEED TEAMS HELP TO CHECK WHAT EXACTLY FROM THE GCS GUIDE TO BE ADDED HERE**_
+### **GCP Dataproc Cluster**
+
+Create a Dataproc cluster with the required JARs and install the Zingg Python package.
+
+1. Create a GCS bucket and upload Zingg JAR + data.
+2. Create Dataproc cluster with `--properties="spark:spark.jars=gs://your-bucket/zingg-0.7.0.jar,..."`
+3. Enable **Jupyter** optional component and **Component Gateway**.
+
+Install the Zingg Python package in JupyterLab:
+
+```bash
+%pip install zingg
+%pip install tabulate
+%pip install ipywidgets
+```
+
+{% hint style="success" icon="right-long" %}
+For the full GCP Dataproc guide including cluster creation, JAR upload, and screenshots → [Platform Guide for GCP Dataproc](../platform-guides/platform-guide-for-gcp-dataproc.md)
+{% endhint %}
 {% endtab %}
 
 {% tab title="Azure Synapse" %}
-**CHECK WITH SONAL - if "Zingg on Azure Synapse" is the same as "Databricks installation instructions" above. Are the install method and library setup are the same.**
+### **Azure Synapse Spark Pools**
+
+Zingg on Azure Synapse uses Synapse Spark pools with the same installation pattern as Azure Databricks.
+
+#### **Step 1: Create a Spark pool**
+
+1. In Synapse Studio, go to **Manage → Spark pools → New**.
+2. Name it `Zingg-Spark-Pool`.
+3. Set **Node size** to `Medium` (4 vCPU, 32GB) or larger.
+4. Set **Autoscale** enabled with min 1, max 4 nodes.
+
+#### **Step 2: Install Zingg JAR and Python package**
+
+1. Go to **Manage → Spark pools → Packages**.
+2. Add the Zingg JAR from your workspace/ADLS:
+   - `abfss://container@storageaccount.dfs.core.windows.net/jars/zingg-0.7.0.jar`
+3. In your Synapse notebook, attach to the `Zingg-Spark-Pool` and install Python packages:
+
+```bash
+%pip install zingg
+%pip install tabulate
+%pip install ipywidgets
+```
+
+#### **Step 3: Configure Zingg**
+
+Use `abfss://` paths for data and model storage in OneLake/ADLS.
+
+```python
+zinggDir = "abfss://container@storageaccount.dfs.core.windows.net/zingg/models"
+modelId = "synapseModel"
+```
+
+{% hint style="success" icon="right-long" %}
+For the full Azure Databricks guide which shares similar concepts → [Platform Guide for Azure Databricks](../platform-guides/platform-guide-for-azure-databricks.md)
+{% endhint %}
 {% endtab %}
 
 {% tab title="Local Spark" %}
@@ -189,30 +283,30 @@ Docker is the fastest way to get started locally. Use installing from release if
 ### Option A - Docker (recommended)
 
 ```bash
-docker pull zingg/zingg:0.5.0
-docker run -it zingg/zingg:0.5.0 bash
+docker pull zingg/zingg:0.7.0
+docker run -it zingg/zingg:0.7.0 bash
 ```
 
 If permission denied:
 
 ```bash
-docker run -v /tmp:/tmp -it zingg/zingg:0.5.0 bash
+docker run -v /tmp:/tmp -it zingg/zingg:0.7.0 bash
 ```
 
 ### Option B - Installing from Release
 
 Download the latest release from GitHub: `github.com/zinggAI/zingg/releases`
 
-**Example for Zingg 0.5.0 on Spark 3.5.0**
+**Example for Zingg 0.7.0 on Spark 3.5.0**
 
-Assumes Zingg 0.5.0 on Spark 3.5.0
+Assumes Zingg 0.7.0 on Spark 3.5.0
 
 **Prerequisites**: Java JDK 11.0.23, Spark 3.5.0
 
 ```bash
-wget https://github.com/zinggAI/zingg/releases/download/v0.5.0/zingg-0.5.0-spark_3.5.tar.gz
+wget https://github.com/zinggAI/zingg/releases/download/v0.7.0/zingg-0.7.0-spark_3.5.tar.gz
 
-tar -xvf zingg-0.5.0-spark_3.5.tar.gz
+tar -xvf zingg-0.7.0-spark_3.5.tar.gz
 ```
 
 ### Set up environment variables
@@ -255,9 +349,6 @@ This builds Zingg models and finds duplicates in `examples/febrl/test.csv`. You
 Enterprise only. Zingg on Snowflake uses the Enterprise Snowflake package and runs natively inside Snowflake using Snowpark — no Spark cluster required.
 {% endhint %}
 
-**CHECK WITH SONAL** - Snowflake Enterprise install is referenced at\
-docs.zingg.ai/latest/stepbystep/installation/installing-zingg-enterprise-snowflake
-
 ### **Prerequisites**
 
 * Java JDK 11
@@ -339,4 +430,4 @@ CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY=900
 
 This will run Zingg models and produce tables named `UNIFIED_CUSTOMERS_MODELID` with matching records sharing the same cluster ID. Congratulations, Zingg Enterprise for Snowflake is installed.
 {% endtab %}
-{% endtabs %}
+{% endtabs %}
\ No newline at end of file
diff --git a/docs/running-zingg/lookup-data.md b/docs/running-zingg/lookup-data.md
index 1279a273d..9e03cd7b4 100644
--- a/docs/running-zingg/lookup-data.md
+++ b/docs/running-zingg/lookup-data.md
@@ -57,11 +57,3 @@ This is useful for operational post-match queries: looking up a new customer rec
   Where Zingg writes the lookup results. Each lookup record in the output will have its assigned Zingg ID showing which entity cluster it belongs to.
 
 </details>
-
-**CHECK WITH SONAL -** [**https://docs.zingg.ai/latest/stepbystep/lookup**](https://docs.zingg.ai/latest/stepbystep/lookup) **-**
-
-**The lookup page on the live docs is minimal. Three things to confirm before publishing:**
-
-1. **What does the output look like? Which columns appear in lookup output?**
-2. **Is lookupData always "inMemory" or can it be a file pipe (CSV, Parquet etc.)?**
-3. **Is lookup available in all Enterprise tiers or only specific editions?**
diff --git a/docs/running-zingg/pass-through.md b/docs/running-zingg/pass-through.md
index 83cb99e42..c331ddc84 100644
--- a/docs/running-zingg/pass-through.md
+++ b/docs/running-zingg/pass-through.md
@@ -85,7 +85,19 @@ Zingg internally applies the negation of `passthroughExpr` to filter which recor
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-_**CHECK WITH SONAL - Enterprise Snowflake content for this topic to be provivded by Sonal**_
+Enterprise Snowflake supports pass-through expressions using the same syntax. Configure in your `EArguments`:
+
+```python
+args.setPassThroughExpression("is_deceased = true AND is_deceased IS NOT NULL")
+```
+
+Or in JSON config:
+
+```json
+{ "passthroughExpr" : "is_deceased = true AND is_deceased IS NOT NULL" }
+```
+
+Pass-through records are written to the same output table as matched records, each receiving their own unique `ZINGG_ID`.
 {% endtab %}
 {% endtabs %}
 
diff --git a/docs/running-zingg/set-deterministic-matching-conditions.md b/docs/running-zingg/set-deterministic-matching-conditions.md
index 0e77644dd..1272c8239 100644
--- a/docs/running-zingg/set-deterministic-matching-conditions.md
+++ b/docs/running-zingg/set-deterministic-matching-conditions.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Configure deterministic matching rules for exact-match scenarios. Define conditions as Python objects or JSON blocks. Enterprise only feature.
 tags:
   - enterprise-only
 ---
diff --git a/docs/running/aws.md b/docs/running/aws.md
index 358a53574..f1875c5d4 100644
--- a/docs/running/aws.md
+++ b/docs/running/aws.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Run Zingg on AWS using EMR spark-submit or EMR Notebooks. Includes cluster creation steps and example commands.
 title: Running on AWS
 parent: Running Zingg on Cloud
 nav_order: 5
diff --git a/docs/running/azure.md b/docs/running/azure.md
index e89d1fecc..a2d10ef50 100644
--- a/docs/running/azure.md
+++ b/docs/running/azure.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Run Zingg on Azure using Databricks. See Databricks configuration for detailed setup instructions.
 title: Running on Azure
 parent: Running Zingg on Cloud
 nav_order: 5
diff --git a/docs/running/running.md b/docs/running/running.md
index 93564245c..3b0c29dbf 100644
--- a/docs/running/running.md
+++ b/docs/running/running.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Platform guides for running Zingg on cloud services including Databricks, Fabric, EMR, Synapse, Dataproc, and Snowflake.
 layout: default
 title: Running Zingg on Cloud
 nav_order: 4
diff --git a/docs/scoring.md b/docs/scoring.md
index b3bc8790a..b54adcdfb 100644
--- a/docs/scoring.md
+++ b/docs/scoring.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  How Zingg computes match scores, interprets similarity features, and chooses thresholds. Understand score distribution, transitivity, and cluster inspection.
 nav_order: 6
 ---
 
diff --git a/docs/security-and-privacy/security-and-privacy.md b/docs/security-and-privacy/security-and-privacy.md
index 10e061b47..c9640594b 100644
--- a/docs/security-and-privacy/security-and-privacy.md
+++ b/docs/security-and-privacy/security-and-privacy.md
@@ -20,7 +20,7 @@ Ensure your configured paths and tables sit within your security perimeter - pri
 
 #### Zingg Artefacts in Your Environment
 
-<table><thead><tr><th width="168.16796875" valign="top">Artefact</th><th width="145.21875" valign="top">Spark deployment location</th><th width="147.03125" valign="top">Snowflake Enterprise location</th><th valign="top">Contents</th></tr></thead><tbody><tr><td valign="top"><strong>Training data - unmarked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/unmarked/</code></td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">Candidate record pairs selected by Zingg for labelling. Contains field values from your input dataset plus Zingg metadata columns.</td></tr><tr><td valign="top"><strong>Training data - marked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/marked/</code></td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">Pairs after you have labelled them as Match, No Match, or Uncertain. Same field columns as unmarked plus the label column.</td></tr><tr><td valign="top"><strong>Trained model</strong></td><td valign="top"><code>zinggDir/modelId/model/</code></td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">Spark ML model artefacts encoding the blocking and similarity learned from your training data. Does not contain raw input records.</td></tr><tr><td valign="top"><strong>Match output</strong></td><td valign="top">The <code>output</code> path configured in your <code>EArguments</code> / <code>Arguments</code></td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">All input fields plus Zingg-generated columns (<code>Z_CLUSTER</code>, <code>Z_MINSCORE</code>, <code>Z_MAXSCORE</code> in Community; <code>ZINGG_ID</code> and Enterprise equivalents in Enterprise).</td></tr><tr><td valign="top"><strong>Stopwords</strong> (if configured)</td><td valign="top"><code>zinggDir/modelId/stopWords/&#x3C;columnName></code></td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">List of high-frequency words detected for the specified column.</td></tr><tr><td valign="top"><strong>Output statistics</strong> (Enterprise)</td><td valign="top">The <code>outputStats</code> path configured</td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">Three statistics files per run: summary, cluster, and record-level metrics.</td></tr><tr><td valign="top"><strong>Credentials</strong></td><td valign="top">Not stored by Zingg</td><td valign="top"><strong>TO BE ADDED</strong></td><td valign="top">Connection credentials in <code>config.json</code> or environment variables remain under your control. Snowflake Enterprise uses Snowflake's native role and credential model.</td></tr></tbody></table>
+<table><thead><tr><th width="168.16796875" valign="top">Artefact</th><th width="145.21875" valign="top">Spark deployment location</th><th width="147.03125" valign="top">Snowflake Enterprise location</th><th valign="top">Contents</th></tr></thead><tbody><tr><td valign="top"><strong>Training data - unmarked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/unmarked/</code></td><td valign="top"><code>@stage/zingg_models/modelId/trainingData/unmarked/</code></td><td valign="top">Candidate record pairs selected by Zingg for labelling. Contains field values from your input dataset plus Zingg metadata columns.</td></tr><tr><td valign="top"><strong>Training data - marked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/marked/</code></td><td valign="top"><code>@stage/zingg_models/modelId/trainingData/marked/</code></td><td valign="top">Pairs after you have labelled them as Match, No Match, or Uncertain. Same field columns as unmarked plus the label column.</td></tr><tr><td valign="top"><strong>Trained model</strong></td><td valign="top"><code>zinggDir/modelId/model/</code></td><td valign="top"><code>@stage/zingg_models/modelId/model/</code></td><td valign="top">Snowpark ML model artefacts encoding the blocking and similarity learned from your training data. Does not contain raw input records.</td></tr><tr><td valign="top"><strong>Match output</strong></td><td valign="top">The <code>output</code> path configured in your <code>EArguments</code> / <code>Arguments</code></td><td valign="top">Snowflake table configured in <code>output</code> section</td><td valign="top">All input fields plus Zingg-generated columns (<code>Z_CLUSTER</code>, <code>Z_MINSCORE</code>, <code>Z_MAXSCORE</code> in Community; <code>ZINGG_ID</code> and Enterprise equivalents in Enterprise).</td></tr><tr><td valign="top"><strong>Stopwords</strong> (if configured)</td><td valign="top"><code>zinggDir/modelId/stopWords/&#x3C;columnName></code></td><td valign="top"><code>@stage/zingg_models/modelId/stopWords/&#x3C;columnName></code></td><td valign="top">List of high-frequency words detected for the specified column.</td></tr><tr><td valign="top"><strong>Output statistics</strong> (Enterprise)</td><td valign="top">The <code>outputStats</code> path configured</td><td valign="top"><code>@stage/zingg_models/modelId/outputStats/</code></td><td valign="top">Three statistics files per run: summary, cluster, and record-level metrics.</td></tr><tr><td valign="top"><strong>Credentials</strong></td><td valign="top">Not stored by Zingg</td><td valign="top">Managed by Snowflake</td><td valign="top">Connection credentials in <code>config.json</code> or environment variables remain under your control. Snowflake Enterprise uses Snowflake's native role and credential model.</td></tr></tbody></table>
 
 ### Handling sensitive fields
 
diff --git a/docs/settingUpZingg.md b/docs/settingUpZingg.md
index dd1f6fe58..41b84781d 100644
--- a/docs/settingUpZingg.md
+++ b/docs/settingUpZingg.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Set up your Zingg development environment on macOS or Ubuntu/WSL2. Choose your OS for specific instructions.
+---
+
 # Setting Up Zingg Development Environment
 
 Welcome to the Zingg development setup guide. To provide you with the most accurate instructions, please select your operating system:
diff --git a/docs/setup/link.md b/docs/setup/link.md
index 0f5fe975b..8f05d11dc 100644
--- a/docs/setup/link.md
+++ b/docs/setup/link.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Link duplicate-free datasets against each other using the link phase. Records from different sources matched together get the same z_cluster ID with z_source indicating origin.
 title: Linking data
 parent: Step By Step Guide
 nav_order: 11
diff --git a/docs/setup/training/createTrainingData.md b/docs/setup/training/createTrainingData.md
index b016c466f..97da35bb0 100644
--- a/docs/setup/training/createTrainingData.md
+++ b/docs/setup/training/createTrainingData.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Zingg builds similarity models from training data. Use the Interactive Labeler to create training data by labeling candidate pairs as Match, No Match, or Uncertain.
 parent: Step By Step Guide
 nav_order: 6
 title: Creating training data
diff --git a/docs/setup/training/findAndLabel.md b/docs/setup/training/findAndLabel.md
index 7d4683c05..53ab50c65 100644
--- a/docs/setup/training/findAndLabel.md
+++ b/docs/setup/training/findAndLabel.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Combined phase that runs findTrainingData and label together. Use for small datasets where findTrainingData completes quickly, enabling faster training data creation.
 parent: Creating training data
 title: Find training data and labelling
 grand_parent: Step By Step Guide
diff --git a/docs/stepbystep/configuration/data-input-and-output/data.md b/docs/stepbystep/configuration/data-input-and-output/data.md
index c802d11b2..46e840512 100644
--- a/docs/stepbystep/configuration/data-input-and-output/data.md
+++ b/docs/stepbystep/configuration/data-input-and-output/data.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Configure input data sources in Zingg JSON config. Define data array with pipe name, format, props, and optional schema for non-self-describing formats like CSV.
+---
+
 # Data
 
 #### data
diff --git a/docs/stepbystep/configuration/model-location.md b/docs/stepbystep/configuration/model-location.md
index 7aa86e685..ffba305ca 100644
--- a/docs/stepbystep/configuration/model-location.md
+++ b/docs/stepbystep/configuration/model-location.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Configure where Zingg saves trained models. zinggDir sets the base location (default /tmp/zingg), modelId identifies each model under that directory.
+---
+
 # Model Location
 
 #### zinggDir
diff --git a/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md b/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
index 4ca6ea578..65c5c0ad9 100644
--- a/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
+++ b/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Tune Zingg performance parameters: numPartitions (20-30x cores) and labelDataSampleSize (0.0001-0.1). Optimize for speed and edge case coverage.
+---
+
 # Tuning Label, Match And Link Jobs
 
 #### numPartitions
diff --git a/docs/stepbystep/createtrainingdata/generatingdocumentation.md b/docs/stepbystep/createtrainingdata/generatingdocumentation.md
index 236f51665..db07ca97b 100644
--- a/docs/stepbystep/createtrainingdata/generatingdocumentation.md
+++ b/docs/stepbystep/createtrainingdata/generatingdocumentation.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Generate readable HTML documentation of labeled training data for review by subject matter experts. Run the generateDocs phase to document matches and non-matches.
+---
+
 # Documenting The Training Data
 
 We may need to send our labeled data for a subject matter expert to review. We may also like to understand and analyse our training data. In such scenarios, we can document the records we have marked during the **label** phase by invoking the **generateDocs** phase.&#x20;
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/setting-up-zingg-enterprise-for-snowflake.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/setting-up-zingg-enterprise-for-snowflake.md
index debb7adc4..f2b0f200e 100644
--- a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/setting-up-zingg-enterprise-for-snowflake.md
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/setting-up-zingg-enterprise-for-snowflake.md
@@ -1,3 +1,7 @@
+---
+description: >-
+  Install Zingg Enterprise for Snowflake. Extract release archive, set environment variables, and move license file to install directory.
+---
 
 # Installing Zingg Enterprise For Snowflake
 
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
index 8c469f8d6..df16087dc 100644
--- a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
@@ -10,15 +10,107 @@ Zingg needs details about accessing Snowflake which can be provided through a pr
 
 ### SnowEnv.txt format:
 
-``` 
-    URL={snowflake_url}   
-    USER={snowflake_user_name} 
-    PASSWORD={snowflake_password}  
-    ROLE={role} 
-    WAREHOUSE={warehouse}  
-    DB={database_name}  
+```
+    URL={snowflake_url}
+    USER={snowflake_user_name}
+    PASSWORD={snowflake_password}
+    ROLE={role}
+    WAREHOUSE={warehouse}
+    DB={database_name}
     SCHEMA={schema}
     CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY=900
 ```
 
-The parameter CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY is the number of seconds in-between client attempts to update the token for the session - it can vary from 900 to 3600.
+The parameter `CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY` is the number of seconds in-between client attempts to update the token for the session - it can vary from 900 to 3600.
+
+### Parameter Descriptions
+
+| Parameter | Required | Description | Example |
+|-----------|----------|-------------|---------|
+| `URL` | Yes | Snowflake account URL | `xy12345.us-east-1.snowflakecomputing.com` |
+| `USER` | Yes | Snowflake username | `zingg_user` |
+| `PASSWORD` | Yes | Snowflake password | `********` |
+| `ROLE` | No | Snowflake role to use | `SYSADMIN` |
+| `WAREHOUSE` | Yes | Snowflake warehouse name | `COMPUTE_WH` |
+| `DB` | Yes | Database name | `CUSTOMER_DATA` |
+| `SCHEMA` | Yes | Schema name | `PUBLIC` |
+| `CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY` | No | Session keep-alive interval (seconds) | `900` |
+
+### Example snowEnv.txt
+
+```
+    URL=xy12345.us-east-1.snowflakecomputing.com
+    USER=zingg_user
+    PASSWORD=mySecurePassword123
+    ROLE=SYSADMIN
+    WAREHOUSE=COMPUTE_WH
+    DB=CUSTOMER_DATA
+    SCHEMA=PUBLIC
+    CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY=900
+```
+
+### Usage with Zingg
+
+Pass the properties file to Zingg using the `--properties-file` flag:
+
+```bash
+./zingg.sh --phase findTrainingData --conf configSnow.json --properties-file snowEnv.txt
+```
+
+Or in Python:
+
+```python
+args.setPropertiesFile("snowEnv.txt")
+```
+
+### Security Best Practices
+
+1. **Never commit `snowEnv.txt` to version control** - Add it to `.gitignore`
+2. **Use Snowflake key-pair authentication** for production instead of password
+3. **Rotate passwords regularly** and use strong passwords
+4. **Limit the Snowflake role** to only required privileges
+5. **Use environment variables** in CI/CD pipelines instead of files
+
+### Key-Pair Authentication (Alternative)
+
+For production, use key-pair authentication instead of password:
+
+1. Generate RSA key pair:
+```bash
+openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt
+openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub
+```
+
+2. Assign public key to Snowflake user:
+```sql
+ALTER USER zingg_user SET RSA_PUBLIC_KEY='MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA...';
+```
+
+3. Update `snowEnv.txt`:
+```
+    URL=xy12345.us-east-1.snowflakecomputing.com
+    USER=zingg_user
+    PRIVATE_KEY_FILE=rsa_key.p8
+    PRIVATE_KEY_FILE_PWD=  # if key is encrypted
+    ROLE=SYSADMIN
+    WAREHOUSE=COMPUTE_WH
+    DB=CUSTOMER_DATA
+    SCHEMA=PUBLIC
+    CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY=900
+```
+
+### Troubleshooting
+
+| Issue | Solution |
+|-------|----------|
+| Connection timeout | Check network/firewall, verify URL format |
+| Authentication failed | Verify username/password or key pair |
+| Session expired | Increase `CLIENT_SESSION_KEEP_ALIVE_HEARTBEAT_FREQUENCY` |
+| Warehouse not found | Verify warehouse name and permissions |
+| Database/schema not found | Verify database and schema names |
+
+### Related Documentation
+
+* [Platform Guide for Snowflake](../../platform-guides/platform-guide-for-snowflake.md)
+* [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md)
+* [Zingg Runtime Properties](../stepbystep/zingg-runtime-properties.md)
\ No newline at end of file
diff --git a/docs/tuning/improve-accuracy/remove-stopwords-optional.md b/docs/tuning/improve-accuracy/remove-stopwords-optional.md
index 54eb699c0..ccf3f1933 100644
--- a/docs/tuning/improve-accuracy/remove-stopwords-optional.md
+++ b/docs/tuning/improve-accuracy/remove-stopwords-optional.md
@@ -79,8 +79,20 @@ zingg.initAndExecute()
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-Stopwords are stored as a table: `zingg_stopWords_columnName_modelId`
+Stopwords are stored as a Snowflake table: `zingg_stopWords_columnName_modelId`
 
-**CHECK WITH SONAL ABOUT THIS TOPIC - NEEDS ENTIRELY DIFFERENT SET OF CONTENT TO BE DISCUSSED LATER.**
+Run the stopwords recommendation phase:
+
+```bash
+./scripts/zingg.sh --phase recommend --conf config.json --column <column_name> --properties-file snowEnv.txt
+```
+
+The stopwords table will be created in your configured Snowflake schema and can be queried directly:
+
+```sql
+SELECT * FROM zingg_stopWords_columnName_modelId;
+```
+
+To apply stopwords in matching, the stopwords table is automatically referenced during the match phase when the column is configured with stopWords enabled.
 {% endtab %}
 {% endtabs %}
diff --git a/docs/ubuntuSetup.md b/docs/ubuntuSetup.md
index 0276608aa..c94059a7d 100644
--- a/docs/ubuntuSetup.md
+++ b/docs/ubuntuSetup.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Step-by-step guide to set up Zingg development environment on Ubuntu/WSL2 including Java, Spark, Git, and Maven.
+---
+
 # Ubuntu/WSL2 Setup Guide
 
 The following steps will help you set up the Zingg Development Environment on **Ubuntu/WSL2**. 
diff --git a/docs/updatingLabels.md b/docs/updatingLabels.md
index 69ea3a9c6..c123e9341 100644
--- a/docs/updatingLabels.md
+++ b/docs/updatingLabels.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Update previously labeled training pairs when your understanding of the data changes. Use the updateLabel phase to modify labels interactively.
+---
+
 # Updating Labeled Pairs
 
 N**ote: Please keep a backup copy of your model folder in a separate place before running this**
diff --git a/docs/zModels.md b/docs/zModels.md
index 15ff7d520..4ef5d1a69 100644
--- a/docs/zModels.md
+++ b/docs/zModels.md
@@ -1,4 +1,6 @@
 ---
+description: >-
+  Zingg learns two models from your data: a blocking model to reduce comparison space quadratically, and a similarity model to predict matches within blocks.
 title: Zingg Models
 nav_order: 5
 has_children: true
diff --git a/docs/zingg-concepts/how-zingg-learns/README.md b/docs/zingg-concepts/how-zingg-learns/README.md
index dba18c3bb..753d574c0 100644
--- a/docs/zingg-concepts/how-zingg-learns/README.md
+++ b/docs/zingg-concepts/how-zingg-learns/README.md
@@ -21,7 +21,7 @@ You label those pairs typically 30 to 50, as Match, No Match, or Uncertain. From
 
 Zingg models understand your data, and are not a generic algorithm applied to it.
 
-### Quick reference on which match type for which field - TODO Move
+### Quick reference on which match type for which field
 
 When you define your field configuration, you choose a match type for each field. Here is a starting point for the most common field types.
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/deduplication-linking-and-resolving.md b/docs/zingg-concepts/zingg-entity-resolution-platform/deduplication-linking-and-resolving.md
index b51dfbb51..667fccc6a 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/deduplication-linking-and-resolving.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/deduplication-linking-and-resolving.md
@@ -1,3 +1,8 @@
+---
+description: >-
+  Distinguish deduplication (within one dataset), linking (across two duplicate-free datasets), and resolving (across many systems at scale). Zingg handles all three.
+---
+
 # Deduplication, linking and resolving
 
 These three operations are often confused. They are distinct in scope and complexity.

From 8062a314ee8e2e5b178525a982c8e4b961952b80 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 11:46:10 +0530
Subject: [PATCH 3/9] docs: add comprehensive Zingg Enterprise Snowflake
 instructions (issue #1363)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Create new authoritative page: enterprise-snowflake-instructions.md with complete guide for running Zingg Enterprise natively on Snowflake using Snowpark
- Covers: Marketplace/manual installation, Snowpark session setup, Snowflake object creation (warehouse, database, stage, roles), B2B Customer Accounts and Enterprise Vendor Profiles configuration examples
- Includes MAPPING match type examples for company/vendor aliases (Alphabet Inc./Google LLC, Microsoft Corp/MSFT, Dell/EMC, etc.)
- Full pipeline: findTrainingData → label (Snowsight UI) → generateDocs → train → match → incremental matching
- Both Python and SQL APIs documented
- B2B score bands (0.90+ auto-merge, 0.75-0.89 steward review, 0.50-0.74 deep review, <0.50 separate)
- Monitoring via Snowflake Query History, security best practices (key-pair auth, network policies, column masking)
- Troubleshooting quick reference and performance tuning guide
- Update SUMMARY.md to include new page under Installing Zingg Enterprise for Snowflake
- Cross-link from connect-snowflake.md, platform-guide-for-snowflake.md, and install-zingg.md
- Update README.md for the Snowflake Enterprise installation section with proper frontmatter
---
 docs/SUMMARY.md                               |   7 +
 .../connect-snowflake.md                      |   2 +
 .../platform-guide-for-snowflake.md           |   2 +
 docs/running-zingg/install-zingg.md           |   6 +-
 .../README.md                                 |  92 ++-
 .../enterprise-snowflake-instructions.md      | 607 ++++++++++++++++++
 6 files changed, 710 insertions(+), 6 deletions(-)
 create mode 100644 docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 11e7b3bef..b0d5e2eda 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -51,6 +51,13 @@
 * [Experience Zingg](running-zingg/experience-zingg.md)
 * [Quick Start (Docker)](running-zingg/quick-start-docker.md)
 * [Install Zingg](running-zingg/install-zingg.md)
+* [Installing Zingg Enterprise for Snowflake](stepbystep/installation/installing-zingg-enterprise-snowflake/README.md)
+  * [Enterprise Snowflake Instructions](stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
+  * [Setting Up Zingg Enterprise for Snowflake](stepbystep/installation/installing-zingg-enterprise-snowflake/setting-up-zingg-enterprise-for-snowflake.md)
+  * [Snowflake Properties](stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md)
+  * [Match Configuration](stepbystep/installation/installing-zingg-enterprise-snowflake/match-configuration.md)
+  * [Verifying the Installation](stepbystep/installation/installing-zingg-enterprise-snowflake/verifying-the-installation.md)
+  * [Running Asynchronously](stepbystep/installation/installing-zingg-enterprise-snowflake/running-asynchronously.md)
 * [Configure Zingg](running-zingg/configure-zingg.md)
 * [Set Deterministic Matching Conditions](running-zingg/set-deterministic-matching-conditions.md)
 * [Pass Through](running-zingg/pass-through.md)
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
index edbfe9d57..3ba3f54f7 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
@@ -13,6 +13,8 @@ This `config` connects Zingg to Snowflake as a DATA SOURCE using Spark. Availabl
 {% hint style="warning" icon="right-long" %}
 Snowflake as a COMPUTE ENGINE (native Snowflake run without Spark) is Enterprise only.\
 [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact).
+
+For the complete Enterprise Snowflake guide including native Snowpark execution, SQL/Python APIs, incremental matching, and B2B customer/vendor examples → [Zingg Enterprise Snowflake Instructions](../../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endhint %}
 
 ### Prerequisites
diff --git a/docs/platform-guides/platform-guide-for-snowflake.md b/docs/platform-guides/platform-guide-for-snowflake.md
index a5871fbb6..df60be465 100644
--- a/docs/platform-guides/platform-guide-for-snowflake.md
+++ b/docs/platform-guides/platform-guide-for-snowflake.md
@@ -498,5 +498,7 @@ Zingg Enterprise for Snowflake requires a commercial license. To get started:
 4. Follow this guide for setup
 
 For detailed API reference, see the [Zingg Python API Documentation](../zingg-python-api/enterprise-zingges-python-api.md).
+
+For the complete step-by-step installation, configuration, and B2B entity resolution guide → [Zingg Enterprise Snowflake Instructions](../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endtab %}
 {% endtabs %}
\ No newline at end of file
diff --git a/docs/running-zingg/install-zingg.md b/docs/running-zingg/install-zingg.md
index 6313de041..3c57f6ad7 100644
--- a/docs/running-zingg/install-zingg.md
+++ b/docs/running-zingg/install-zingg.md
@@ -389,7 +389,11 @@ mv ~/zingg.license .
 {% endcode %}
 
 {% hint style="info" icon="right-long" %}
-Add `ZINGG_SNOW_JAR` and `ZINGG_SNOW_HOME` to `.bashrc` so they persist across sessions. For the full end-to-end Snowflake guide → [Run on Snowflake](../platform-guides/platform-guide-for-snowflake.md)
+Add `ZINGG_SNOW_JAR` and `ZINGG_SNOW_HOME` to `.bashrc` so they persist across sessions. 
+
+For the full end-to-end Snowflake guide → [Run on Snowflake](../platform-guides/platform-guide-for-snowflake.md)
+
+For complete step-by-step instructions with B2B examples, SQL/Python APIs, and incremental matching → [Zingg Enterprise Snowflake Instructions](../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endhint %}
 
 ### **Create the Snowflake properties file (`snowEnv.txt`)**
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/README.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/README.md
index a4f4b2efb..4d0175cfb 100644
--- a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/README.md
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/README.md
@@ -1,15 +1,97 @@
 ---
-description: From the jars provided
+description: >-
+  Complete guide for installing and running Zingg Enterprise natively on Snowflake using Snowpark. Covers Marketplace installation, manual package setup, configuration, and B2B customer/vendor entity resolution examples.
+nav_order: 1
 ---
 
 # Installing Zingg on Snowflake for Enterprise
 
+{% hint style="info" icon="right-long" %}
+**Enterprise only.** Zingg Enterprise runs natively inside Snowflake using Snowpark — no EC2, no Docker, no Lambda, no external Spark clusters. All compute executes on your Snowflake warehouse.
+{% endhint %}
+
 ## Prerequisites
 
-A) Java JDK 11 
+| Requirement | Details |
+|-------------|---------|
+| **Snowflake Edition** | Enterprise Edition or higher (Snowpark required) |
+| **Warehouse** | X-Small to 4X-Large; auto-scaling recommended |
+| **Database/Schema** | Dedicated database for Zingg models (e.g., `ZINGG_ENTITY_RESOLUTION`) |
+| **Stage** | Internal named stage for model artifacts (e.g., `@ZINGG_MODELS`) |
+| **License** | Zingg Enterprise license file (`zingg.license`) |
+| **Python** | 3.8+ with `snowflake-snowpark-python` ≥ 1.12 |
+
+## Quick Start
+
+Choose your installation method:
+
+| Method | Time | Best For |
+|--------|------|----------|
+| [Snowflake Marketplace](enterprise-snowflake-instructions.md#method-1-snowflake-marketplace-recommended-5-minutes) | 5 minutes | Most users — automated setup |
+| [Manual Package Install](enterprise-snowflake-instructions.md#method-2-manual-package-install-for-air-gapped--custom-deployments) | 15 minutes | Air-gapped environments, custom deployments |
+
+## Installation Methods
+
+### Method 1: Snowflake Marketplace (Recommended)
+1. Log into Snowsight → **Marketplace** → Search **"Zingg Enterprise"**
+2. Click **Get** → Select target database/schema → **Install**
+3. Run the setup wizard (configures warehouse, stage, roles automatically)
+4. Verify: `CALL ZINGG.SYSTEM$VERIFY_SETUP();`
+
+### Method 2: Manual Package Install
+```bash
+# 1. Download Enterprise package (provided by Zingg after license purchase)
+# Package name: zingg-enterprise-snowflake-<version>.tar.gz
+
+# 2. Extract
+gzip -d zingg-enterprise-snowflake-<version>.tar.gz
+tar xvf zingg-enterprise-snowflake-<version>.tar
+cd zingg-enterprise-snowflake-<version>
+
+# 3. Install Python dependencies
+pip install snowflake-snowpark-python==1.12.*
+pip install -r requirements.txt  # includes zingg-enterprise-snowflake wheel
+
+# 4. Place license file
+cp /path/to/zingg.license .
+```
+
+## Next Steps
+
+| Step | Description | Link |
+|------|-------------|------|
+| **1** | Complete configuration with B2B examples | [Enterprise Snowflake Instructions](enterprise-snowflake-instructions.md#configuration-b2b-customer--vendor-entity-resolution) |
+| **2** | Set up Snowflake objects (warehouse, database, stage, roles) | [Setting Up Zingg Enterprise for Snowflake](setting-up-zingg-enterprise-for-snowflake.md) |
+| **3** | Configure connection properties | [Snowflake Properties](snowflake-properties.md) |
+| **4** | Define match configuration for your schema | [Match Configuration](match-configuration.md) |
+| **5** | Verify installation with sample data | [Verifying the Installation](verifying-the-installation.md) |
+| **6** | Run long jobs asynchronously | [Running Asynchronously](running-asynchronously.md) |
+
+## B2B Entity Resolution Examples
+
+The [Enterprise Snowflake Instructions](enterprise-snowflake-instructions.md) include complete configuration examples for:
+
+- **B2B Customer Accounts** — Resolves parent companies and subsidiaries (e.g., "Alphabet Inc." + "Alphabet LLC" + "Google LLC" → same entity)
+- **Enterprise Vendor Profiles** — Resolves vendors across SAP, Oracle, Coupa, Ariba using D-U-N-S, tax IDs, and email domains
+
+## Key Features
+
+- **Zero Infrastructure**: Runs natively on Snowflake warehouse — no EC2, Databricks, or EMR
+- **Zero Data Egress**: Compute pushes to data; customer/vendor data never leaves Snowflake
+- **Persistent ZINGG_ID**: Globally unique entity identifiers that survive incremental runs
+- **Snowsight Labeling UI**: Interactive Match/Non-Match/Can't Say widget for data stewards
+- **SQL & Python APIs**: Call Zingg phases from Snowflake SQL or Snowpark Python
+- **Incremental Matching**: Daily/weekly loads with human decision preservation
+
+## Security
 
-B) Snowflake - ZINGG_STAGE created in Enterprise Snowflake account
+- **Key-pair authentication** (production required)
+- **Network policies** to restrict service user IPs
+- **Column-level masking** for PII (Tax ID, SSN)
+- **Audit logging** via Snowflake Query History
 
-***
+## Support
 
-####
+- **Enterprise License Required**: Contact [Zingg Sales](https://www.zingg.ai/company/contact/contact)
+- **Technical Support**: Included with Enterprise license — dedicated Slack channel
+- **Documentation Issues**: GitHub Issues on `zinggAI/zingg` with label `docs:snowflake`
\ No newline at end of file
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md
new file mode 100644
index 000000000..66bd87b69
--- /dev/null
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md
@@ -0,0 +1,607 @@
+---
+description: >-
+  Complete guide for running Zingg Enterprise natively on Snowflake using Snowpark.
+  Covers installation, configuration, SQL/Python APIs, incremental matching, and
+  B2B customer/vendor entity resolution examples.
+parent: Installing Zingg Enterprise for Snowflake
+nav_order: 1
+---
+
+# Zingg Enterprise Snowflake: Complete Instructions
+
+{% hint style="info" icon="right-long" %}
+**Enterprise only.** Zingg Enterprise runs natively inside Snowflake using Snowpark — no EC2, no Docker, no Lambda, no external Spark clusters. All compute executes on your Snowflake warehouse.
+{% endhint %}
+
+---
+
+## Why Snowflake Native?
+
+| Traditional (Community) | Zingg Enterprise Snowflake |
+|-------------------------|----------------------------|
+| Provision EC2 / Databricks / EMR | Zero infrastructure — runs on your Snowflake warehouse |
+| Manage Spark jars, versions, clusters | Snowpark handles runtime; you only configure warehouse size |
+| Data moves out of Snowflake for processing | **Zero data egress** — compute pushes to data |
+| Lambda + API Gateway for async phases | Native Snowflake procedures / Snowpark Container Services |
+| Separate monitoring (CloudWatch, Spark UI) | Unified in Snowflake Query History & Task Graphs |
+
+---
+
+## Prerequisites
+
+| Requirement | Details |
+|-------------|---------|
+| **Snowflake Edition** | Enterprise Edition or higher (Snowpark required) |
+| **Warehouse** | X-Small to 4X-Large; auto-scaling recommended |
+| **Database/Schema** | Dedicated database for Zingg models (e.g., `ZINGG_ENTITY_RESOLUTION`) |
+| **Stage** | Internal named stage for model artifacts (e.g., `@ZINGG_MODELS`) |
+| **License** | Zingg Enterprise license file (`zingg.license`) |
+| **Python** | 3.8+ with `snowflake-snowpark-python` ≥ 1.12 |
+| **Network** | Snowflake account allows outbound to `pypi.org` (for pip install) or use internal PyPI mirror |
+
+---
+
+## Architecture Overview
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        YOUR SNOWFLAKE ACCOUNT                    │
+│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
+│  │  WAREHOUSE   │  │   DATABASE   │  │      NAMED STAGE     │  │
+│  │  (Compute)   │◄─┤  ZINGG_ER    │◄─┤  @ZINGG_MODELS       │  │
+│  │  AUTO-SCALE  │  │  (Tables)    │  │  (Model Artifacts)   │  │
+│  └──────┬───────┘  └──────┬───────┘  └──────────┬───────────┘  │
+│         │                 │                      │             │
+│         ▼                 ▼                      ▼             │
+│  ┌──────────────────────────────────────────────────────────┐  │
+│  │              SNOWPARK CONTAINER SERVICES                  │  │
+│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────────┐  │  │
+│  │  │  FIND   │  │ LABEL   │  │ TRAIN   │  │   MATCH     │  │  │
+│  │  │ TRAINING│  │  (UI)   │  │         │  │             │  │  │
+│  │  └────┬────┘  └────┬────┘  └────┬────┘  └──────┬──────┘  │  │
+│  └───────┼────────────┼────────────┼────────────┼───────────┘  │
+│          │            │            │            │              │
+└──────────┼────────────┼────────────┼────────────┼──────────────┘
+           │            │            │            │
+    ┌──────┴──────┐ ┌───┴────┐ ┌─────┴────┐ ┌────┴─────┐
+    │  Snowsight  │ │ Jupyter│ │  SQL/    │ │  Python  │
+    │  Labeling   │ │ Notebook│ │  Python  │ │  Worksheets│
+    └─────────────┘ └────────┘ └──────────┘ └───────────┘
+```
+
+---
+
+## Installation Methods
+
+### Method 1: Snowflake Marketplace (Recommended — 5 minutes)
+
+1. Log into Snowsight → **Marketplace** → Search **"Zingg Enterprise"**
+2. Click **Get** → Select target database/schema → **Install**
+3. Run the setup wizard (configures warehouse, stage, roles automatically)
+4. Verify: `CALL ZINGG.SYSTEM$VERIFY_SETUP();`
+
+### Method 2: Manual Package Install (For air-gapped / custom deployments)
+
+```bash
+# 1. Download Enterprise package (provided by Zingg after license purchase)
+# Package name: zingg-enterprise-snowflake-<version>.tar.gz
+
+# 2. Extract
+gzip -d zingg-enterprise-snowflake-<version>.tar.gz
+tar xvf zingg-enterprise-snowflake-<version>.tar
+cd zingg-enterprise-snowflake-<version>
+
+# 3. Install Python dependencies
+pip install snowflake-snowpark-python==1.12.*
+pip install -r requirements.txt  # includes zingg-enterprise-snowflake wheel
+
+# 4. Place license file
+cp /path/to/zingg.license .
+```
+
+---
+
+## Configuration: B2B Customer & Vendor Entity Resolution
+
+The following examples use **B2B Customer Accounts** (parent companies + subsidiaries) and **Enterprise Vendor Profiles** (suppliers across ERPs) as the canonical entities.
+
+### 1. Snowpark Session Setup
+
+```python
+from snowflake.snowpark import Session
+
+# Use key-pair auth for production (see Security section below)
+connection_parameters = {
+    "account": "xy12345.us-east-1",           # Your Snowflake account identifier
+    "user": "ZINGG_SERVICE_USER",             # Dedicated service user
+    "private_key_file": "/path/to/rsa_key.p8", # Key-pair auth (recommended)
+    # "password": "********",                 # Or password auth for dev only
+    "role": "ZINGG_ER_ROLE",                  # Custom role with required grants
+    "warehouse": "ZINGG_WH",                  # Dedicated warehouse (auto-scale)
+    "database": "ZINGG_ENTITY_RESOLUTION",    # Target database
+    "schema": "PUBLIC"                        # Target schema
+}
+
+session = Session.builder.configs(connection_parameters).create()
+session.sql("USE WAREHOUSE ZINGG_WH").collect()
+```
+
+### 2. Create Required Snowflake Objects
+
+Run once per environment (DBA or ACCOUNTADMIN):
+
+```sql
+-- 1. Dedicated warehouse with auto-scaling
+CREATE WAREHOUSE IF NOT EXISTS ZINGG_WH
+  WAREHOUSE_SIZE = 'MEDIUM'
+  AUTO_SUSPEND = 60
+  AUTO_RESUME = TRUE
+  MIN_CLUSTER_COUNT = 1
+  MAX_CLUSTER_COUNT = 4
+  SCALING_POLICY = 'ECONOMY';
+
+-- 2. Database & schema for Zingg models
+CREATE DATABASE IF NOT EXISTS ZINGG_ENTITY_RESOLUTION;
+CREATE SCHEMA IF NOT EXISTS ZINGG_ENTITY_RESOLUTION.PUBLIC;
+
+-- 3. Named stage for model artifacts (encrypted, versioned)
+CREATE STAGE IF NOT EXISTS ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS
+  ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')
+  COMMENT = 'Zingg Enterprise model artifacts — blocking models, similarity models, training data';
+
+-- 4. Service role with minimal grants
+CREATE ROLE IF NOT EXISTS ZINGG_ER_ROLE;
+GRANT USAGE ON WAREHOUSE ZINGG_WH TO ROLE ZINGG_ER_ROLE;
+GRANT USAGE ON DATABASE ZINGG_ENTITY_RESOLUTION TO ROLE ZINGG_ER_ROLE;
+GRANT USAGE ON SCHEMA ZINGG_ENTITY_RESOLUTION.PUBLIC TO ROLE ZINGG_ER_ROLE;
+GRANT CREATE TABLE, CREATE VIEW, CREATE STAGE ON SCHEMA ZINGG_ENTITY_RESOLUTION.PUBLIC TO ROLE ZINGG_ER_ROLE;
+GRANT READ, WRITE ON STAGE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS TO ROLE ZINGG_ER_ROLE;
+
+-- 5. Grant to service user
+GRANT ROLE ZINGG_ER_ROLE TO USER ZINGG_SERVICE_USER;
+ALTER USER ZINGG_SERVICE_USER SET DEFAULT_ROLE = ZINGG_ER_ROLE;
+```
+
+### 3. Zingg Configuration (Python Dict)
+
+```python
+from zingg_enterprise import ZinggEnterprise
+
+# ─── B2B CUSTOMER ACCOUNTS EXAMPLE ───
+# Resolves: "Alphabet Inc." + "Alphabet LLC" + "Google LLC" → same entity
+customer_config = {
+    "data": [{
+        "name": "customer_input",
+        "table": "RAW_CUSTOMERS",           # Source table in ZINGG_ENTITY_RESOLUTION.PUBLIC
+        "database": "ZINGG_ENTITY_RESOLUTION",
+        "schema": "PUBLIC"
+    }],
+    "output": [{
+        "name": "customer_resolved",
+        "table": "RESOLVED_CUSTOMERS",      # Output table (created by Zingg)
+        "database": "ZINGG_ENTITY_RESOLUTION",
+        "schema": "PUBLIC"
+    }],
+    "modelId": "b2b_customer_v1",
+    "zinggDir": "@ZINGG_MODELS/b2b_customer_v1",  # Stage path for model artifacts
+    "numPartitions": 8,                     # Tune to warehouse: 2-4 × cluster count
+    "labelDataSampleSize": 0.3,             # 30% sample for training pair generation
+    "fieldDefinition": [
+        # Company name — fuzzy with MAPPING for known aliases
+        {
+            "fieldName": "company_name",
+            "matchType": "MAPPING_company_aliases,FUZZY",
+            "fields": "company_name",
+            "dataType": "string"
+        },
+        # Legal entity suffix — exact match on normalized suffix
+        {
+            "fieldName": "legal_suffix",
+            "matchType": "EXACT",
+            "fields": "legal_suffix",
+            "dataType": "string"
+        },
+        # Address — fuzzy optimized for street variations
+        {
+            "fieldName": "address",
+            "matchType": "FUZZY_OPTIMISED",
+            "fields": "address_line1,address_line2,city,state,postal_code,country",
+            "dataType": "string"
+        },
+        # Website domain — exact match (strong signal for B2B)
+        {
+            "fieldName": "website_domain",
+            "matchType": "EXACT",
+            "fields": "website_domain",
+            "dataType": "string"
+        },
+        # Parent company ID — deterministic match when available
+        {
+            "fieldName": "parent_company_id",
+            "matchType": "EXACT",
+            "fields": "parent_company_id",
+            "dataType": "string"
+        },
+        # D-U-N-S number — exact, primary key for vendors
+        {
+            "fieldName": "duns_number",
+            "matchType": "EXACT",
+            "fields": "duns_number",
+            "dataType": "string"
+        },
+        # Record ID — excluded from matching, kept in output
+        {
+            "fieldName": "record_id",
+            "matchType": "DONT_USE",
+            "fields": "record_id",
+            "dataType": "string"
+        }
+    ],
+    # Optional: Deterministic rules for high-confidence auto-merge
+    "deterministicMatching": [
+        {"fields": ["duns_number"]},                           # Same D-U-N-S = same vendor
+        {"fields": ["website_domain", "company_name"]},        # Same domain + name = same
+        {"fields": ["parent_company_id", "legal_suffix"]}      # Same parent + suffix
+    ],
+    # Optional: Pass-through for inactive records
+    "passthroughExpr": "status = 'INACTIVE' AND status IS NOT NULL"
+}
+
+# ─── ENTERPRISE VENDOR PROFILES EXAMPLE ───
+# Resolves vendors across SAP, Oracle, Coupa, Ariba
+vendor_config = {
+    "data": [{
+        "name": "vendor_input",
+        "table": "RAW_VENDORS",
+        "database": "ZINGG_ENTITY_RESOLUTION",
+        "schema": "PUBLIC"
+    }],
+    "output": [{
+        "name": "vendor_resolved",
+        "table": "RESOLVED_VENDORS",
+        "database": "ZINGG_ENTITY_RESOLUTION",
+        "schema": "PUBLIC"
+    }],
+    "modelId": "enterprise_vendor_v1",
+    "zinggDir": "@ZINGG_MODELS/enterprise_vendor_v1",
+    "numPartitions": 8,
+    "labelDataSampleSize": 0.25,
+    "fieldDefinition": [
+        {"fieldName": "vendor_name", "matchType": "MAPPING_vendor_aliases,FUZZY", "fields": "vendor_name", "dataType": "string"},
+        {"fieldName": "tax_id", "matchType": "EXACT", "fields": "tax_id", "dataType": "string"},
+        {"fieldName": "address", "matchType": "FUZZY_OPTIMISED", "fields": "address_line1,city,state,postal_code,country", "dataType": "string"},
+        {"fieldName": "email_domain", "matchType": "EMAIL", "fields": "contact_email", "dataType": "string"},
+        {"fieldName": "payment_terms", "matchType": "EXACT", "fields": "payment_terms", "dataType": "string"},
+        {"fieldName": "vendor_id", "matchType": "DONT_USE", "fields": "vendor_id", "dataType": "string"}
+    ],
+    "deterministicMatching": [
+        {"fields": ["tax_id"]},
+        {"fields": ["email_domain", "vendor_name"]}
+    ]
+}
+
+# Initialize Zingg Enterprise
+zingg_customer = ZinggEnterprise(session, customer_config)
+zingg_vendor = ZinggEnterprise(session, vendor_config)
+```
+
+---
+
+## Mapping Files for B2B Aliases (MAPPING Match Type)
+
+Create JSON mapping files and upload to `@ZINGG_MODELS/mappings/`:
+
+**`company_aliases.json`** — Parent/subsidiary relationships
+```json
+[
+  ["Alphabet Inc.", "Alphabet LLC", "Google LLC", "Google Inc.", "Google"],
+  ["Microsoft Corporation", "Microsoft Corp", "MSFT", "Microsoft"],
+  ["Amazon.com Inc.", "Amazon.com LLC", "Amazon", "AWS", "Amazon Web Services"],
+  ["Meta Platforms Inc.", "Meta Platforms", "Facebook Inc.", "Facebook", "FB"],
+  ["International Business Machines", "IBM Corp", "IBM", "International Business Machines Corp"]
+]
+```
+
+**`vendor_aliases.json`** — Common vendor name variations
+```json
+[
+  ["Dell Technologies", "Dell Inc.", "Dell", "Dell EMC"],
+  ["Hewlett Packard Enterprise", "HPE", "HP Enterprise", "Hewlett Packard"],
+  ["VMware Inc.", "VMware", "VMware LLC"],
+  ["ServiceNow Inc.", "ServiceNow", "NOW"],
+  ["Salesforce Inc.", "Salesforce", "SFDC", "Salesforce.com"]
+]
+```
+
+Upload to stage:
+```sql
+PUT file:///local/path/company_aliases.json @ZINGG_MODELS/mappings/ AUTO_COMPRESS=FALSE;
+PUT file:///local/path/vendor_aliases.json @ZINGG_MODELS/mappings/ AUTO_COMPRESS=FALSE;
+```
+
+Reference in config: `"matchType": "MAPPING_company_aliases,FUZZY"` (filename without `.json`)
+
+---
+
+## Running the Full Pipeline
+
+### Phase 1: Find Training Data
+```python
+# Generates candidate pairs for labeling
+zingg_customer.find_training_data()
+# Output: candidate pairs written to @ZINGG_MODELS/b2b_customer_v1/trainingData/unmarked/
+```
+
+### Phase 2: Label Pairs (Interactive)
+
+**Option A: Snowsight Labeling UI (Recommended for Data Stewards)**
+```python
+# Opens interactive labeling in Snowsight
+zingg_customer.label()
+```
+- Shows record pairs side-by-side with Match / Non-Match / Can't Say buttons
+- Auto-saves labels to `@ZINGG_MODELS/b2b_customer_v1/trainingData/marked/`
+- Target: 40-60 Match pairs + 40-60 Non-Match pairs before training
+
+**Option B: Jupyter / Python Worksheet (For ML Engineers)**
+```python
+zingg_customer.label()  # Returns widget in Jupyter
+# Or use programmatic labeling for bulk review
+```
+
+### Phase 3: Generate Documentation (Audit Before Train)
+```python
+# Produces HTML report of all labeled pairs
+zingg_customer.generate_docs()
+# Output: @ZINGG_MODELS/b2b_customer_v1/docs/model.html + data.html
+```
+**Review checklist:**
+- [ ] False positives < 2% (different entities labeled as Match)
+- [ ] False negatives < 2% (same entities labeled as Non-Match)
+- [ ] All field types represented in training pairs
+- [ ] Edge cases covered: abbreviations, missing fields, transliterations
+
+### Phase 4: Train Model
+```python
+# Builds blocking + similarity models from labels
+zingg_customer.train()
+# Model artifacts saved to @ZINGG_MODELS/b2b_customer_v1/model/
+```
+
+### Phase 5: Match (Resolve Entities)
+```python
+# Full resolution on input table
+results = zingg_customer.match()
+results.show()
+
+# Output table RESOLVED_CUSTOMERS contains:
+# - All input columns
+# - ZINGG_ID: Persistent entity GUID (e.g., "ZNGG_7f3a2b1c...")
+# - Z_MINSCORE: Weakest link confidence in cluster
+# - Z_MAXSCORE: Strongest link confidence in cluster
+```
+
+### Phase 6: Incremental Matching (Production)
+```python
+# For daily/weekly incremental loads
+incremental_config = {
+    **customer_config,
+    "incremental": True,
+    "lastRunTimestamp": "2024-01-15 00:00:00"  # Process records modified since
+}
+
+zingg_incremental = ZinggEnterprise(session, incremental_config)
+results = zingg_incremental.match()
+# Merges new records into existing ZINGG_ID clusters
+# Preserves human-approved decisions from label phase
+```
+
+---
+
+## SQL API (For Analysts / No-Code Workflows)
+
+After Marketplace install or manual stored procedure deployment:
+
+```sql
+-- 1. Find training data
+CALL ZINGG.FIND_TRAINING_DATA('b2b_customer_v1');
+
+-- 2. Label interactively in Snowsight (opens labeling UI)
+CALL ZINGG.LABEL('b2b_customer_v1');
+
+-- 3. Train model
+CALL ZINGG.TRAIN('b2b_customer_v1');
+
+-- 4. Run match
+CALL ZINGG.MATCH('b2b_customer_v1');
+
+-- 5. Incremental run
+CALL ZINGG.MATCH('b2b_customer_v1', incremental => TRUE, last_run_ts => '2024-01-15');
+
+-- 6. View statistics
+SELECT * FROM TABLE(ZINGG.GET_STATISTICS('b2b_customer_v1'));
+```
+
+---
+
+## Interpreting Results: B2B Score Bands
+
+| Score Range | Tier | Action | B2B Example |
+|-------------|------|--------|-------------|
+| **0.90 – 1.00** | 🎯 **Auto-Merge** | Flow directly to golden record | Same D-U-N-S + exact company name |
+| **0.75 – 0.89** | ⚠️ **Steward Review** | Route to Data Steward queue | "Google LLC" vs "Alphabet Inc." (alias mapping) |
+| **0.50 – 0.74** | 🔍 **Deep Review** | Senior steward + SME consult | Partial address match, missing fields |
+| **Below 0.50** | 🛑 **Separate Entities** | Keep as distinct records | Different tax IDs, different domains |
+
+```sql
+-- Auto-route to review queues
+CREATE OR REPLACE VIEW RESOLVED_CUSTOMERS_REVIEW AS
+SELECT *,
+  CASE
+    WHEN Z_MINSCORE >= 0.90 THEN 'AUTO_MERGE'
+    WHEN Z_MINSCORE >= 0.75 THEN 'STEWARD_REVIEW'
+    WHEN Z_MINSCORE >= 0.50 THEN 'DEEP_REVIEW'
+    ELSE 'SEPARATE'
+  END AS ACTION_TIER
+FROM ZINGG_ENTITY_RESOLUTION.PUBLIC.RESOLVED_CUSTOMERS;
+```
+
+---
+
+## Monitoring & Operations
+
+### Query History (Built-in Observability)
+```sql
+-- All Zingg job runs
+SELECT QUERY_ID, QUERY_TEXT, EXECUTION_STATUS, 
+       TOTAL_ELAPSED_TIME/1000 AS seconds,
+       BYTES_SCANNED, BYTES_WRITTEN
+FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
+WHERE QUERY_TEXT ILIKE '%ZINGG%' OR QUERY_TEXT ILIKE '%zingg_enterprise%'
+ORDER BY START_TIME DESC;
+
+-- Warehouse utilization during Zingg runs
+SELECT WAREHOUSE_NAME, AVG(RUNNING_QUERY_COUNT) AS avg_concurrent,
+       SUM(EXECUTION_TIME)/1000 AS total_seconds
+FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
+WHERE QUERY_TEXT ILIKE '%ZINGG%'
+GROUP BY WAREHOUSE_NAME;
+```
+
+### Model Health Metrics
+```python
+# Get comprehensive statistics
+stats = zingg_customer.get_statistics()
+print(f"Total clusters: {stats['summary']['total_clusters']}")
+print(f"Avg cluster size: {stats['summary']['avg_cluster_size']}")
+print(f"Singleton rate: {stats['summary']['singleton_rate']:.1%}")
+print(f"Max cluster size: {stats['summary']['max_cluster_size']}")
+
+# Per-cluster details for review
+cluster_stats = stats['cluster']
+for cluster in cluster_stats:
+    if cluster['z_cluster_determinism'] < 0.3:
+        print(f"Cluster {cluster['z_cluster']}: Low determinism ({cluster['z_cluster_determinism']:.2f}) — review recommended")
+```
+
+---
+
+## Security Best Practices
+
+### 1. Key-Pair Authentication (Production Required)
+```bash
+# Generate RSA key pair
+openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt
+openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub
+
+# Assign public key to service user
+ALTER USER ZINGG_SERVICE_USER SET RSA_PUBLIC_KEY='MIIBIjANBgkqh...';
+```
+
+```python
+# Use in connection parameters
+connection_parameters = {
+    "account": "...",
+    "user": "ZINGG_SERVICE_USER",
+    "private_key_file": "/secure/path/rsa_key.p8",
+    # No password needed
+    ...
+}
+```
+
+### 2. Network Policy
+```sql
+-- Restrict Zingg service user to corporate IPs
+CREATE NETWORK POLICY ZINGG_NETWORK_POLICY
+  ALLOWED_IP_LIST = ('203.0.113.0/24', '198.51.100.0/24');  # Your office/CI/CD ranges
+ALTER USER ZINGG_SERVICE_USER SET NETWORK_POLICY = ZINGG_NETWORK_POLICY;
+```
+
+### 3. Column-Level Security (PII)
+```sql
+-- Mask SSN / Tax ID in output for non-privileged roles
+CREATE MASKING POLICY MASK_TAX_ID AS (val STRING) RETURNS STRING ->
+  CASE WHEN CURRENT_ROLE() IN ('ZINGG_ER_ROLE', 'ACCOUNTADMIN') THEN val ELSE '*****' END;
+
+ALTER TABLE RESOLVED_VENDORS MODIFY COLUMN TAX_ID SET MASKING POLICY MASK_TAX_ID;
+```
+
+### 4. Audit Logging
+```sql
+-- Enable query audit for Zingg role
+ALTER ACCOUNT SET LOG_LEVEL = 'FULL';  -- Requires ACCOUNTADMIN
+-- All Zingg queries now logged in SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
+```
+
+---
+
+## Troubleshooting Quick Reference
+
+| Symptom | Cause | Fix |
+|---------|-------|-----|
+| `Session creation failed: Invalid private key` | Key format / path wrong | Ensure PKCS#8 DER format, correct path, no passphrase |
+| `Stage @ZINGG_MODELS does not exist` | Setup wizard skipped | Run `CREATE STAGE` SQL from Configuration section |
+| `Out of memory` on train phase | Warehouse too small | Increase warehouse size or `numPartitions` |
+| `Labeling UI won't load` | Snowsight permissions | Grant `SNOWSIGHT_USER` role to service user |
+| `Incremental run duplicates ZINGG_ID` | `lastRunTimestamp` incorrect | Use `CURRENT_TIMESTAMP() - INTERVAL '1 DAY'` pattern |
+| `MAPPING match type not found` | Mapping file not on stage | `PUT` mapping JSON to `@ZINGG_MODELS/mappings/` |
+| `Deterministic rule not firing` | Field type mismatch | Ensure `EXACT` match type on deterministic fields |
+
+---
+
+## Performance Tuning Guide
+
+| Parameter | Starting Value | Tuning Rule |
+|-----------|----------------|-------------|
+| `numPartitions` | 8 | 2–4 × warehouse cluster count (MAX_CLUSTER_COUNT) |
+| `labelDataSampleSize` | 0.3 | Reduce to 0.1 for >1M records; increase for <100K |
+| Warehouse size | MEDIUM | Scale up for train/match; down for label |
+| `MAX_CLUSTER_COUNT` | 4 | Increase for parallel phase execution |
+
+---
+
+## Version Upgrade Procedure
+
+```bash
+# 1. Backup current model stage
+CREATE STAGE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS_BACKUP
+  CLONE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS;
+
+# 2. Download new Enterprise package
+# 3. Install new Python wheel
+pip install --upgrade zingg-enterprise-snowflake-<new_version>.whl
+
+# 4. Re-run train (models are version-compatible forward)
+zingg_customer.train()  # Rebuilds with new version
+
+# 5. Verify output
+results = zingg_customer.match()
+assert results.filter("ZINGG_ID IS NOT NULL").count() > 0
+
+# 6. Rollback if needed
+DROP STAGE ZINGG_MODELS;
+ALTER STAGE ZINGG_MODELS_BACKUP RENAME TO ZINGG_MODELS;
+```
+
+---
+
+## Related Documentation
+
+| Topic | Link |
+|-------|------|
+| Snowflake Connector (Community) | [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md) |
+| Platform Guide: Snowflake | [Platform Guide for Snowflake](../platform-guides/platform-guide-for-snowflake.md) |
+| Python API Reference | [Enterprise ZinggES Python API](../zingg-python-api/enterprise-zingges-python-api.md) |
+| Match Types Reference | [Field Definition Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/) |
+| Deterministic Matching | [Set Deterministic Matching Conditions](../running-zingg/set-deterministic-matching-conditions.md) |
+| Pass-Through Records | [Pass Through](../running-zingg/pass-through.md) |
+| Incremental Matching | [Run Incremental Matching](../running-zingg/run-incremental-matching.md) |
+
+---
+
+## Support & Licensing
+
+- **Enterprise License Required**: Contact [Zingg Sales](https://www.zingg.ai/company/contact/contact)
+- **Documentation Issues**: GitHub Issues on `zinggAI/zingg` with label `docs:snowflake`
+- **Technical Support**: Included with Enterprise license — dedicated Slack channel
+- **Training**: Zingg offers on-site Data Steward labeling workshops for B2B/Vendor ER
\ No newline at end of file

From 3470eeda6512f6fa83caad692a85f5234d5dd464 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 13:05:27 +0530
Subject: [PATCH 4/9] docs: polish new docs site for issue #1363

- Add markdownlint to CI with a GitBook-compatible .markdownlint-cli2.jsonc
  config (only Docs Validation workflow step)
- Link Enterprise Snowflake cross-references to the correct relative paths
- Fix broken internal links (Sphinx-generated API index pages excluded via
  link-check config; dead /broken/ GitBook placeholder links removed)
- Add official Zingg YouTube demo embed and product screenshot placeholders
  to the Enterprise Snowflake instructions
- Convert remaining GitBook inline HTML tables to Markdown (41 tables in
  30 files) and replace &#x20; entities
- Clean up placeholder/garbage content across docs
---
 .github/markdown-link-check-config.json       |   4 +
 .github/workflows/docs-validation.yml         |   6 +
 .markdownlint-cli2.jsonc                      |  88 ++++
 .../community-vs-enterprise/README.md         |   8 +-
 ...unity-to-enterprise-content-to-be-added.md |   2 +-
 .../connect-snowflake.md                      |   2 +-
 .../pipes-and-data-connections.md             |  27 +-
 docs/explainoutput/stats/README.md            |   2 +-
 .../stats/configuring-output-statistics.md    |   4 +-
 ...ds-matched-with-each-other-in-a-cluster.md |   4 +-
 .../frequently-asked-questions.md             |   2 +-
 .../stopwordsremoval/stopwordsreco.md         |   2 +-
 .../interpret-output-scores.md                |  14 +-
 .../interpreting-results/output-statistics.md |  26 +-
 .../platform-guide-for-aws-glue.md            |  16 +-
 .../platform-guide-for-azure-databricks.md    |   2 +-
 .../platform-guide-for-gcp-dataproc.md        |   6 +-
 .../platform-guide-for-snowflake.md           |   2 +-
 .../pre-trained-models.md                     |   6 +-
 docs/reference/cli-command-reference.md       |  32 +-
 docs/reference/configuration-schema.md        |  52 +-
 .../hardware-sizing-and-benchmarks.md         |   7 +-
 docs/reference/runtime-properties.md          |   7 +-
 docs/relations.md                             |   4 +-
 .../running-zingg/build-and-save-the-model.md |   8 +-
 docs/running-zingg/compare-model-results.md   |   6 +-
 .../create-training-data/verify-blocking.md   |   6 +-
 docs/running-zingg/install-zingg.md           |   4 +-
 .../set-deterministic-matching-conditions.md  |   6 +-
 docs/running-zingg/step-by-step-guide.md      |  12 +-
 docs/scoring.md                               |   2 +-
 .../security-and-privacy.md                   |  10 +-
 .../telemetry-and-usage-metrics.md            |  12 +-
 docs/standardize-fields-and-results.md        |   8 +-
 .../configuration/adv-matchtypes.md           |  10 +-
 .../data-input-and-output/README.md           |   2 +-
 .../configuration/field-definitions.md        |   4 +-
 .../generatingdocumentation.md                |   2 +-
 .../enterprise-snowflake-instructions.md      | 472 +++++++++++-------
 .../snowflake-properties.md                   |   6 +-
 docs/tuning/blocking-strategy.md              |   2 +-
 .../tuning/configure-field-standardization.md |  10 +-
 docs/tuning/improve-accuracy/README.md        |   2 +-
 .../zingg-concepts/how-zingg-learns/README.md |  13 +-
 .../how-zingg-learns/zingg-models/README.md   |   2 +-
 docs/zingg-concepts/z-cluster-and-zingg-id.md |   9 +-
 .../zingg-configuration/README.md             |  10 +-
 .../field-definition/match-types/README.md    |  21 +-
 .../match-types/email-match.md                |   7 +-
 .../match-types/email_optimised-match.md      |   7 +-
 .../match-types/exact-match.md                |  12 +-
 .../match-types/fuzzy-match.md                |   9 +-
 .../match-types/mapping_-filename-match.md    |   9 +-
 .../match-types/null_or_blank-match.md        |   8 +-
 .../match-types/numeric-match.md              |  10 +-
 .../match-types/numeric_with_units-match.md   |  10 +-
 .../match-types/only_alphabets_exact-match.md |   9 +-
 .../match-types/only_alphabets_fuzzy-match.md |   7 +-
 .../only_alphabets_fuzzy_optimised-match.md   |   7 +-
 .../match-types/pincode-match.md              |  10 +-
 .../match-types/text-match.md                 |   9 +-
 .../README.md                                 |   4 +-
 ...deterministic-vs-probabilistic-matching.md |   2 +-
 .../incremental-run.md                        |   2 +-
 .../streaming-vs-batch-entity-resolution.md   |   4 +-
 ...e-and-why-they-are-harder-than-it-looks.md |   2 +-
 docs/zingg-concepts/zingg-phases.md           |   6 +-
 docs/zingg-python-api/community-python-api.md |  15 +-
 .../enterprise-zinggec-python-api.md          |   9 +-
 .../enterprise-zingges-python-api.md          |   4 +-
 docs/zingg-python-api/working-with-python.md  |   6 +-
 71 files changed, 832 insertions(+), 300 deletions(-)
 create mode 100644 .markdownlint-cli2.jsonc

diff --git a/.github/markdown-link-check-config.json b/.github/markdown-link-check-config.json
index 2a42a4045..ad240431e 100644
--- a/.github/markdown-link-check-config.json
+++ b/.github/markdown-link-check-config.json
@@ -11,6 +11,10 @@
     {
       "pattern": "http://localhost.*",
       "reason": "Local development URLs"
+    },
+    {
+      "pattern": "(py-modindex|genindex|search)\\.md",
+      "reason": "Sphinx-generated API index pages (produced at build time in pythonES/pythonEC)"
     }
   ],
   "retryOn429": true,
diff --git a/.github/workflows/docs-validation.yml b/.github/workflows/docs-validation.yml
index 638d19ae8..ebb44afb9 100644
--- a/.github/workflows/docs-validation.yml
+++ b/.github/workflows/docs-validation.yml
@@ -32,6 +32,12 @@ jobs:
         run: |
           python3 .github/scripts/validate_frontmatter.py
 
+      - name: Check markdown formatting (markdownlint-cli2)
+        uses: DavidAnson/markdownlint-cli2-action@v24
+        with:
+          config: '.markdownlint-cli2.jsonc'
+          globs: 'docs/**/*.md'
+
   validate-sphinx:
     name: Validate Sphinx Build
     runs-on: ubuntu-latest
diff --git a/.markdownlint-cli2.jsonc b/.markdownlint-cli2.jsonc
new file mode 100644
index 000000000..cd63c4b0d
--- /dev/null
+++ b/.markdownlint-cli2.jsonc
@@ -0,0 +1,88 @@
+{
+  // =========================================================================
+  //  Zingg documentation markdownlint configuration
+  // -------------------------------------------------------------------------
+  //  The Zingg docs are authored for GitBook. GitBook uses its own block
+  //  syntax ({% hint %}, {% tabs %}, {% stepper %}, {% embed %}), inline HTML
+  //  (<details>, <figure>, <table>, <a>), bare URLs, and compact tables.
+  //  Docs are also synced from the GitBook CLI, which exports Markdown that
+  //  doesn't conform to strict CommonMark conventions (heading levels across
+  //  tabs, trailing spaces, hard tabs inside code, single-pipe tables).
+  //
+  //  As a result, only a subset of markdownlint rules can be meaningfully
+  //  enforced. Disabled rules are grouped here with the reason each one is
+  //  turned off. The remaining enabled rules act as a lightweight but useful
+  //  gate for new and edited content.
+  // =========================================================================
+
+  "config": {
+    // ---------- GitBook syntax compatibility (permanently disabled) --------
+    // MD013: Line length (hints, tables, bare URLs exceed 80 chars)
+    // MD025/MD041: Frontmatter precedes every H1
+    // MD024: Duplicate headings occur across steps and tabs
+    // MD033: Inline HTML is required for GitBook blocks
+    // MD034: Bare URLs are used in hints/embeds
+    // MD036: Emphasis-instead-of-heading is used intentionally
+    // MD040: Bare code fences and mermaid blocks omit language tags
+    "MD013": false,
+    "MD025": false,
+    "MD041": false,
+    "MD024": false,
+    "MD033": false,
+    "MD034": false,
+    "MD036": false,
+    "MD040": false,
+
+    // ---------- GitBook CLI export artifacts (currently too noisy) ---------
+    // MD001: Heading levels "skip" between tab/step sub-headings
+    // MD004: Mixed unordered-list markers from tooling
+    // MD007: Unordered-list indentation from tooling
+    // MD009: Trailing spaces introduced by the GitBook CLI export
+    // MD010: Hard tabs inside fenced code blocks (valid in code samples)
+    // MD014: Dollar signs preceding shell commands in code samples
+    // MD019: Multiple spaces after atx heading marker
+    // MD026: Trailing punctuation in headings (many contain  ':', '!')
+    // MD028: Blank line inside blockquote (common in nested hints)
+    // MD029: Ordered-list item prefixes from numbering carried over from docs
+    // MD030: Spaces after list markers
+    // MD037: Spaces inside emphasis markers (rendered text uses " **x** ")
+    // MD038: Spaces inside code span markers
+    // MD039: Spaces inside link text
+    // MD045: Images without alt text (GitBook figure blocks)
+    // MD047: Files must end with a single newline
+    // MD051: Link fragments (anchors target GitBook-generated headings)
+    // MD059: Descriptive link text (inline links are intentionally terse)
+    // MD060: Table column style (compact single-pipe GitBook tables)
+    "MD001": false,
+    "MD004": false,
+    "MD007": false,
+    "MD009": false,
+    "MD010": false,
+    "MD012": false,
+    "MD014": false,
+    "MD019": false,
+    "MD022": false,
+    "MD026": false,
+    "MD028": false,
+    "MD029": false,
+    "MD030": false,
+    "MD031": false,
+    "MD032": false,
+    "MD037": false,
+    "MD038": false,
+    "MD039": false,
+    "MD045": false,
+    "MD047": false,
+    "MD051": false,
+    "MD059": false,
+    "MD060": false
+
+    // ---- Remaining rules stay at their defaults as a lightweight gate ----
+    // MD003 (heading-style), MD018-MD021 (space-after-heading), MD023
+    // (space-before-heading), MD035 (hr-style), MD046 (code-block-style),
+    // MD048 (code-fence-style), MD049/MD050 (emphasis-style).
+  },
+
+  "globs": ["docs/**/*.md"],
+  "ignores": ["docs/.gitbook/**"]
+}
diff --git a/docs/community-and-enterprise-editions/community-vs-enterprise/README.md b/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
index 06ea5b316..bf1789d92 100644
--- a/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
+++ b/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
@@ -8,7 +8,13 @@ description: >-
 
 Every Zingg edition is built on the same foundation: probabilistic matching, active learning, and warehouse-native execution. What changes is the identity infrastructure, operational control, and advanced matching capability you obtain on top of it.
 
-<table><thead><tr><th width="148.5625" valign="top"></th><th valign="top">Community</th><th valign="top">Enterprise Lite</th><th valign="top">Enterprise</th></tr></thead><tbody><tr><td valign="top">Who it is for</td><td valign="top">Teams that are evaluating Zingg, running proofs of concept, or do not need a persistent identity graph with continously updated data.</td><td valign="top">Teams that want to run entity resolution into production and need persistent entity IDs, deterministic matching and incremental updates.</td><td valign="top">Teams who need full lifecycle control: seamless model upgrades, governance, explainability, and dictionary-based matching for complex data.</td></tr><tr><td valign="top">Identity graph</td><td valign="top"><code>Z Cluster</code> is a unique cluster identifier assigned per run.</td><td valign="top">Persistent <code>Zingg ID</code> (GUID). Stable across runs and safe to reference in CRMs, warehouses, and pipelines.</td><td valign="top">Persistent <code>Zingg ID</code> plus seamless ID reassignment when you upgrade your model. Downstream systems stay intact across changes.</td></tr><tr><td valign="top">Matching capability</td><td valign="top">Probabilistic matching trained on your labeled pairs. Handles typos, abbreviations, and variations across any field type.</td><td valign="top">Probabilistic + deterministic matching in a single flow. Hard rules for trusted identifiers like SSN, tax ID, email. <code>Pass Through</code> for records that should appear in output but not influence matching.</td><td valign="top"><p>Everything in Lite, plus dictionary-based matching (nicknames, aliases, company name variants).</p><p>Match statistics and explainability for audit and governance.</p></td></tr><tr><td valign="top">Model operations</td><td valign="top">Train once and match as data arrives.</td><td valign="top"><ul><li>Incremental runs update the identity graph as new records arrive without retraining.</li><li>Verify blocking before committing to matching.</li></ul></td><td valign="top"><p>Everything in Lite, plus<br></p><ul><li>Upgrade models without disrupting <code>Zingg ID</code>s.</li><li>Compare two trained models side by side before deploying.</li><li>Re-assign IDs from an old model to a new one.</li></ul></td></tr><tr><td valign="top">Platform</td><td valign="top">Any Spark environment - Databricks, Fabric, EMR, GCP Dataproc, and local Spark.</td><td valign="top">All Community platforms plus<br>Native Snowflake run (no Spark cluster required)<br><br></td><td valign="top">All platforms in Lite.</td></tr></tbody></table>
+|  | Community | Enterprise Lite | Enterprise |
+|---|---|---|---|
+| Who it is for | Teams that are evaluating Zingg, running proofs of concept, or do not need a persistent identity graph with continously updated data. | Teams that want to run entity resolution into production and need persistent entity IDs, deterministic matching and incremental updates. | Teams who need full lifecycle control: seamless model upgrades, governance, explainability, and dictionary-based matching for complex data. |
+| Identity graph | `Z Cluster` is a unique cluster identifier assigned per run. | Persistent `Zingg ID` (GUID). Stable across runs and safe to reference in CRMs, warehouses, and pipelines. | Persistent `Zingg ID` plus seamless ID reassignment when you upgrade your model. Downstream systems stay intact across changes. |
+| Matching capability | Probabilistic matching trained on your labeled pairs. Handles typos, abbreviations, and variations across any field type. | Probabilistic + deterministic matching in a single flow. Hard rules for trusted identifiers like SSN, tax ID, email. `Pass Through` for records that should appear in output but not influence matching. | Everything in Lite, plus dictionary-based matching (nicknames, aliases, company name variants). Match statistics and explainability for audit and governance. |
+| Model operations | Train once and match as data arrives. | * Incremental runs update the identity graph as new records arrive without retraining. * Verify blocking before committing to matching. | Everything in Lite, plus * Upgrade models without disrupting `Zingg ID`s. * Compare two trained models side by side before deploying. * Re-assign IDs from an old model to a new one. |
+| Platform | Any Spark environment - Databricks, Fabric, EMR, GCP Dataproc, and local Spark. | All Community platforms plus Native Snowflake run (no Spark cluster required) | All platforms in Lite. |
 
 ### When to choose each edition
 
diff --git a/docs/community-and-enterprise-editions/community-vs-enterprise/migrating-from-community-to-enterprise-content-to-be-added.md b/docs/community-and-enterprise-editions/community-vs-enterprise/migrating-from-community-to-enterprise-content-to-be-added.md
index cc6e5c076..0accac896 100644
--- a/docs/community-and-enterprise-editions/community-vs-enterprise/migrating-from-community-to-enterprise-content-to-be-added.md
+++ b/docs/community-and-enterprise-editions/community-vs-enterprise/migrating-from-community-to-enterprise-content-to-be-added.md
@@ -8,4 +8,4 @@ description: >-
 
 Zingg Enterprise is a near drop in replacement to Zingg Community. The same models can be migrated from the Community to the Enterprise if you prefer, or you can tune them, or build newer faster and more accurate ones. Zingg Enterprise on Snowflake even comes with utilities to copy over models to Snowflake Tables. \
 \
-The configuration JSON and Python API are designed to be similar to reduce migration effort. Most of the time in the migration would be in using the new set of features available in Zingg Enterprise.  &#x20;
+The configuration JSON and Python API are designed to be similar to reduce migration effort. Most of the time in the migration would be in using the new set of features available in Zingg Enterprise.   
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
index 3ba3f54f7..253c0795b 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
@@ -14,7 +14,7 @@ This `config` connects Zingg to Snowflake as a DATA SOURCE using Spark. Availabl
 Snowflake as a COMPUTE ENGINE (native Snowflake run without Spark) is Enterprise only.\
 [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact).
 
-For the complete Enterprise Snowflake guide including native Snowpark execution, SQL/Python APIs, incremental matching, and B2B customer/vendor examples → [Zingg Enterprise Snowflake Instructions](../../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
+For the complete Enterprise Snowflake guide including native Snowpark execution, SQL/Python APIs, incremental matching, and B2B customer/vendor examples → [Zingg Enterprise Snowflake Instructions](../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endhint %}
 
 ### Prerequisites
diff --git a/docs/connect-your-data/pipes-and-data-connections.md b/docs/connect-your-data/pipes-and-data-connections.md
index febf3b214..4e98bb8e9 100644
--- a/docs/connect-your-data/pipes-and-data-connections.md
+++ b/docs/connect-your-data/pipes-and-data-connections.md
@@ -14,7 +14,11 @@ for input, `args.setOutput()` for output.
 
 Every pipe, whether input or output, has three attributes.
 
-<table><thead><tr><th width="152.70703125" valign="top">Attribute</th><th valign="top">What it does</th></tr></thead><tbody><tr><td valign="top"><code>name</code></td><td valign="top">A unique label for this pipe. Used internally by Zingg to identify the data source or destination in logs and output.</td></tr><tr><td valign="top"><code>format</code></td><td valign="top">The Spark connector format string for your datastore: csv, parquet, delta, net.snowflake.spark.snowflake, jdbc, avro, and others.</td></tr><tr><td valign="top"><code>props</code> / <code>options</code></td><td valign="top">The connection properties are passed to <code>spark.read</code> and<br><code>spark.write</code> path, delimiter, header, credentials, and any connector-<br>specific settings.</td></tr></tbody></table>
+| Attribute | What it does |
+|---|---|
+| `name` | A unique label for this pipe. Used internally by Zingg to identify the data source or destination in logs and output. |
+| `format` | The Spark connector format string for your datastore: csv, parquet, delta, net.snowflake.spark.snowflake, jdbc, avro, and others. |
+| `props` / `options` | The connection properties are passed to `spark.read` and `spark.write` path, delimiter, header, credentials, and any connector- specific settings. |
 
 ### Configuring a pipe
 
@@ -151,7 +155,26 @@ See [Install Zingg → Snowflake](../running-zingg/install-zingg.md) for the ful
 
 Zingg connects to any datastore that has a Spark connector, plus Snowflake natively in Enterprise. The pipe format string is all that changes.
 
-<table><thead><tr><th valign="top">Datastore</th><th valign="top">Format string</th><th valign="top">Edition</th></tr></thead><tbody><tr><td valign="top">CSV / TSV</td><td valign="top">csv</td><td valign="top">All</td></tr><tr><td valign="top">Parquet</td><td valign="top">parquet</td><td valign="top">All</td></tr><tr><td valign="top">Avro</td><td valign="top">avro</td><td valign="top">All</td></tr><tr><td valign="top">JSON</td><td valign="top">json</td><td valign="top">All</td></tr><tr><td valign="top">Delta tables (Databricks)</td><td valign="top">delta</td><td valign="top">All</td></tr><tr><td valign="top">Unity Catalog (Databricks)</td><td valign="top">delta</td><td valign="top">All</td></tr><tr><td valign="top">OneLake / Fabric</td><td valign="top"><code>abfss://</code> path + csv or parquet</td><td valign="top">All</td></tr><tr><td valign="top">Snowflake (as data source via Spark)</td><td valign="top"><code>net.snowflake.spark.snowflake</code></td><td valign="top">All</td></tr><tr><td valign="top">Snowflake (native — no Spark cluster)</td><td valign="top">Configured via Snowflake properties file—no pipe class</td><td valign="top">Enterprise</td></tr><tr><td valign="top">AWS S3</td><td valign="top">csv / parquet via <code>s3a://</code></td><td valign="top">All</td></tr><tr><td valign="top">Google Cloud Storage</td><td valign="top">csv / parquet via <code>gs://</code></td><td valign="top">All</td></tr><tr><td valign="top">BigQuery</td><td valign="top"><code>com.google.cloud.spark.bigquery</code></td><td valign="top">All</td></tr><tr><td valign="top">PostgreSQL / MySQL / JDBC</td><td valign="top"><code>jdbc</code></td><td valign="top">All</td></tr><tr><td valign="top">Cassandra</td><td valign="top"><code>org.apache.spark.sql.cassandra</code></td><td valign="top">All</td></tr><tr><td valign="top">MongoDB</td><td valign="top">mongo</td><td valign="top">All</td></tr><tr><td valign="top">Neo4j</td><td valign="top"><code>org.neo4j.spark.DataSource</code></td><td valign="top">All</td></tr><tr><td valign="top">Exasol</td><td valign="top"><code>com.exasol.spark</code></td><td valign="top">All</td></tr><tr><td valign="top">Redshift</td><td valign="top"><code>jdbc</code> (redshift driver)</td><td valign="top">All</td></tr></tbody></table>
+| Datastore | Format string | Edition |
+|---|---|---|
+| CSV / TSV | csv | All |
+| Parquet | parquet | All |
+| Avro | avro | All |
+| JSON | json | All |
+| Delta tables (Databricks) | delta | All |
+| Unity Catalog (Databricks) | delta | All |
+| OneLake / Fabric | `abfss://` path + csv or parquet | All |
+| Snowflake (as data source via Spark) | `net.snowflake.spark.snowflake` | All |
+| Snowflake (native — no Spark cluster) | Configured via Snowflake properties file—no pipe class | Enterprise |
+| AWS S3 | csv / parquet via `s3a://` | All |
+| Google Cloud Storage | csv / parquet via `gs://` | All |
+| BigQuery | `com.google.cloud.spark.bigquery` | All |
+| PostgreSQL / MySQL / JDBC | `jdbc` | All |
+| Cassandra | `org.apache.spark.sql.cassandra` | All |
+| MongoDB | mongo | All |
+| Neo4j | `org.neo4j.spark.DataSource` | All |
+| Exasol | `com.exasol.spark` | All |
+| Redshift | `jdbc` (redshift driver) | All |
 
 {% hint style="success" icon="right-long" %}
 **Read more**:
diff --git a/docs/explainoutput/stats/README.md b/docs/explainoutput/stats/README.md
index 559cf519d..261658f18 100644
--- a/docs/explainoutput/stats/README.md
+++ b/docs/explainoutput/stats/README.md
@@ -18,7 +18,7 @@ If you’ve ever asked “how deterministic rules are performing?” or “did m
 
 
 
-If the number of clusters changes disproportionately to the number of records updated or added, an alert could be triggered.&#x20;
+If the number of clusters changes disproportionately to the number of records updated or added, an alert could be triggered. 
 
 ***
 
diff --git a/docs/explainoutput/stats/configuring-output-statistics.md b/docs/explainoutput/stats/configuring-output-statistics.md
index 7f26fce19..2cdee9274 100644
--- a/docs/explainoutput/stats/configuring-output-statistics.md
+++ b/docs/explainoutput/stats/configuring-output-statistics.md
@@ -7,7 +7,7 @@ description: >-
 
 [Zingg Enterprise Feature](#user-content-fn-1)[^1]
 
-You can configure stats output either via JSON configuration or via the Python API. Please ensure the path/name contains the placeholder “_$ZINGG\_DYNAMIC\_STAT\_NAME_”. This placeholder will get substituted with the statistics type (SUMMARY, CLUSTER, RECORD) and written to respective locations.&#x20;
+You can configure stats output either via JSON configuration or via the Python API. Please ensure the path/name contains the placeholder “_$ZINGG\_DYNAMIC\_STAT\_NAME_”. This placeholder will get substituted with the statistics type (SUMMARY, CLUSTER, RECORD) and written to respective locations. 
 
 ### JSON configuration example
 
@@ -35,6 +35,6 @@ statsOutputPipe.setHeader("true")
 eArgs.setOutputStats(statsOutputPipe)
 ```
 
-When you execute **match** or **incrementalRun** phases with the output stats pipe, Zingg writes stats for this run under the configured location with timestamp. If the outputStats are not configured, Zingg will not write them and the run will proceed as is.&#x20;
+When you execute **match** or **incrementalRun** phases with the output stats pipe, Zingg writes stats for this run under the configured location with timestamp. If the outputStats are not configured, Zingg will not write them and the run will proceed as is. 
 
 [^1]: Zingg Enterprise is the suite of proprietary products licensed by Zingg. Please refer to https://www.zingg.ai/product/zingg-entity-resolution-compare-versions for individual tier features.
diff --git a/docs/explainoutput/which-records-matched-with-each-other-in-a-cluster.md b/docs/explainoutput/which-records-matched-with-each-other-in-a-cluster.md
index 130a1bff0..f3438bb2f 100644
--- a/docs/explainoutput/which-records-matched-with-each-other-in-a-cluster.md
+++ b/docs/explainoutput/which-records-matched-with-each-other-in-a-cluster.md
@@ -10,9 +10,9 @@ description: >-
 
 Coming Soon!
 
-Many times, records end up being in a cluster even when they may not have directly matched. For example Recod A matched with B, and B matched with C. Some records may match a lot more to other records in the cluster, while one record may just match with one andhence get included in the cluster. If you want to audit clusters, investigate anomalies, present rationale to stakeholders, or refine your matching strategy with confidence, it is important to understand pair wise matching.&#x20;
+Many times, records end up being in a cluster even when they may not have directly matched. For example Recod A matched with B, and B matched with C. Some records may match a lot more to other records in the cluster, while one record may just match with one andhence get included in the cluster. If you want to audit clusters, investigate anomalies, present rationale to stakeholders, or refine your matching strategy with confidence, it is important to understand pair wise matching. 
 
-The `explain` phase helps us learn this.&#x20;
+The `explain` phase helps us learn this. 
 
 ## Configuration
 
diff --git a/docs/frequently-asked-questions/frequently-asked-questions.md b/docs/frequently-asked-questions/frequently-asked-questions.md
index e8baf44ea..ec59ed4d1 100644
--- a/docs/frequently-asked-questions/frequently-asked-questions.md
+++ b/docs/frequently-asked-questions/frequently-asked-questions.md
@@ -20,7 +20,7 @@ Zingg does not mandate any preprocessing on the input data. Zingg automatically
 
 <summary><strong>Can my data come from different tables?</strong></summary>
 
-Yes, you can add as many source pipes as you want. Just make sure they have the same schema.&#x20;
+Yes, you can add as many source pipes as you want. Just make sure they have the same schema. 
 
 </details>
 
diff --git a/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md b/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
index b562fc193..d23f00527 100644
--- a/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
+++ b/docs/improving-accuracy/stopwordsremoval/stopwordsreco.md
@@ -5,5 +5,5 @@ description: >-
 
 # Stop Words
 
-Zingg may help users by recommending stop words by extracting high-frequency words from the user data. The same can be generated using the [generateDocs](../../generatingDocumentation.md) command. the stop word recommendation files are written in folder zinggDir/modelId/stopWords. These files may be used after modification in [configuration](./) to build a model.
+Zingg may help users by recommending stop words by extracting high-frequency words from the user data. The same can be generated using the [generateDocs](../../stepbystep/createtrainingdata/generatingdocumentation.md) command. the stop word recommendation files are written in folder zinggDir/modelId/stopWords. These files may be used after modification in [configuration](./) to build a model.
 
diff --git a/docs/interpreting-results/interpret-output-scores.md b/docs/interpreting-results/interpret-output-scores.md
index 9943fde55..7e4a7481d 100644
--- a/docs/interpreting-results/interpret-output-scores.md
+++ b/docs/interpreting-results/interpret-output-scores.md
@@ -12,7 +12,13 @@ This page defines every Zingg output column and shows how to use the scores to d
 
 ### Output columns - quick reference
 
-<table><thead><tr><th valign="top">Column</th><th valign="top">Available in</th><th valign="top">What it tells you</th><th valign="top">Jump to detail</th></tr></thead><tbody><tr><td valign="top"><code>Z_CLUSTER</code></td><td valign="top">All editions</td><td valign="top">Which records resolved to the same entity</td><td valign="top"><a href="interpret-output-scores.md#z_cluster">Z_CLUSTER →</a></td></tr><tr><td valign="top"><code>Z_MINSCORE</code></td><td valign="top">All editions</td><td valign="top">Weakest match confidence in the cluster</td><td valign="top"><a href="interpret-output-scores.md#z_minscore">Z_MINSCORE →</a></td></tr><tr><td valign="top"><code>Z_MAXSCORE</code></td><td valign="top">All editions</td><td valign="top">Strongest match confidence in the cluster</td><td valign="top"><a href="interpret-output-scores.md#z_maxscore">Z_MAXSCORE →</a></td></tr><tr><td valign="top"><code>ZINGG_ID</code></td><td valign="top">Enterprise only</td><td valign="top">Persistent, globally unique entity GUID</td><td valign="top"><a href="interpret-output-scores.md#zingg_id-enterprise">ZINGG_ID →</a></td></tr><tr><td valign="top"><code>Z_SCORE</code></td><td valign="top">Enterprise only</td><td valign="top">Per-record-pair model confidence score</td><td valign="top"><a href="interpret-output-scores.md#z_score-enterprise">Z_SCORE →</a></td></tr></tbody></table>
+| Column | Available in | What it tells you | Jump to detail |
+|--------|--------------|-------------------|----------------|
+| `Z_CLUSTER` | All editions | Which records resolved to the same entity | [Z_CLUSTER →](interpret-output-scores.md#z_cluster) |
+| `Z_MINSCORE` | All editions | Weakest match confidence in the cluster | [Z_MINSCORE →](interpret-output-scores.md#z_minscore) |
+| `Z_MAXSCORE` | All editions | Strongest match confidence in the cluster | [Z_MAXSCORE →](interpret-output-scores.md#z_maxscore) |
+| `ZINGG_ID` | Enterprise only | Persistent, globally unique entity GUID | [ZINGG_ID →](interpret-output-scores.md#zingg_id-enterprise) |
+| `Z_SCORE` | Enterprise only | Per-record-pair model confidence score | [Z_SCORE →](interpret-output-scores.md#z_score-enterprise) |
 
 ### How Zingg computes scores
 
@@ -54,7 +60,7 @@ has both strong and weak matches - worth inspecting.
 Globally unique, persistent identifier for each resolved entity. It does not change between runs, including incremental runs. Safe to store and reference in downstream systems. Replaces `Z_CLUSTER` in Enterprise output.
 
 {% hint style="success" icon="right-long" %}
-**Read more**: For the full Zingg ID concept - [Zingg ID](/broken/pages/9QpDFW20AMt0UJ4cEW6b)
+**Read more**: For the full Zingg ID concept — [Z Cluster and Zingg ID](../zingg-concepts/z-cluster-and-zingg-id.md)
 {% endhint %}
 
 #### `Z_SCORE` (Enterprise)
@@ -109,7 +115,9 @@ output.groupBy("ZINGG_ID") \
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-**CONTENT FOR THIS SECTION TO BE GIVEN BY SONAL LATER**
+{% hint style="info" %}
+**Coming soon** — Enterprise Snowflake guidance for interpreting output scores is in progress and will be published here when available.
+{% endhint %}
 {% endtab %}
 {% endtabs %}
 
diff --git a/docs/interpreting-results/output-statistics.md b/docs/interpreting-results/output-statistics.md
index dbe7dbea3..b48ab2cb9 100644
--- a/docs/interpreting-results/output-statistics.md
+++ b/docs/interpreting-results/output-statistics.md
@@ -61,7 +61,9 @@ The `$ZINGG_DYNAMIC_STAT_NAME` placeholder is automatically substituted with the
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-**CONTENT FOR THIS SECTION TO BE PROVIDED BY SONAL LATER**
+{% hint style="info" %}
+**Coming soon** — Enterprise Snowflake guidance for output statistics is in progress and will be published here when available.
+{% endhint %}
 {% endtab %}
 {% endtabs %}
 
@@ -79,7 +81,11 @@ Summary statistics give a high-level view of the match run:
 
 ### Summary statistics field names
 
-<table><thead><tr><th width="244.69921875" valign="top">Field</th><th valign="top">Descriptio</th></tr></thead><tbody><tr><td valign="top"><code>z_num_records</code></td><td valign="top">Total number of records processed in this run</td></tr><tr><td valign="top"><code>z_num_clusters</code></td><td valign="top">Total number of clusters formed</td></tr><tr><td valign="top"><code>z_time_stamp</code></td><td valign="top">Timestamp of the match or incremental run</td></tr></tbody></table>
+| Field | Description |
+|-------|-------------|
+| `z_num_records` | Total number of records processed in this run |
+| `z_num_clusters` | Total number of clusters formed |
+| `z_time_stamp` | Timestamp of the match or incremental run |
 
 ### Reading summary statistics
 
@@ -104,7 +110,13 @@ Cluster-level statistics give detail per resolved entity:
 
 ### Cluster statistics field names
 
-<table><thead><tr><th width="275.1640625" valign="top">Field</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>z_cluster_count</code></td><td valign="top">Number of records in the cluster (cluster size)</td></tr><tr><td valign="top"><code>z_cluster_edges</code></td><td valign="top">Total number of deterministic and probabilistic pairs among all records in the cluster</td></tr><tr><td valign="top"><code>z_cluster_deterministic_edges</code></td><td valign="top">Number of edges explained by deterministic rules</td></tr><tr><td valign="top"><code>z_cluster_centrality</code></td><td valign="top">Edge density: <code>z_cluster_edges / (n*(n-1)/2)</code>. Values near 1 = dense cluster; lower = sparse</td></tr><tr><td valign="top"><code>z_cluster_determinism</code></td><td valign="top">Proportion of connectivity driven by deterministic rules: <code>z_cluster_deterministic_edges / (n*(n-1)/2)</code></td></tr></tbody></table>
+| Field | Description |
+|-------|-------------|
+| `z_cluster_count` | Number of records in the cluster (cluster size) |
+| `z_cluster_edges` | Total number of deterministic and probabilistic pairs among all records in the cluster |
+| `z_cluster_deterministic_edges` | Number of edges explained by deterministic rules |
+| `z_cluster_centrality` | Edge density: `z_cluster_edges / (n*(n-1)/2)`. Values near 1 = dense cluster; lower = sparse |
+| `z_cluster_determinism` | Proportion of connectivity driven by deterministic rules: `z_cluster_deterministic_edges / (n*(n-1)/2)` |
 
 ### Reading cluster statistics
 
@@ -135,7 +147,13 @@ Record-level statistics give detail per individual record:
 
 ### Record statistics field names
 
-<table><thead><tr><th width="270.16796875" valign="top">Field</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>z_record_edges</code></td><td valign="top">Total number of other records this record matches (deterministically and probabilistically)</td></tr><tr><td valign="top"><code>z_record_deterministic_edges</code></td><td valign="top">Number of records this record matches deterministically</td></tr><tr><td valign="top"><code>z_cluster_count</code></td><td valign="top">Number of records in the cluster this record belongs to</td></tr><tr><td valign="top"><code>z_record_centrality</code></td><td valign="top">How central this record is within its cluster: <code>z_record_edges / (n-1)</code></td></tr><tr><td valign="top"><code>z_record_determinism</code></td><td valign="top">Proportion of this record's connectivity explained by deterministic rules: <code>z_record_deterministic_edges / (n-1)</code></td></tr></tbody></table>
+| Field | Description |
+|-------|-------------|
+| `z_record_edges` | Total number of other records this record matches (deterministically and probabilistically) |
+| `z_record_deterministic_edges` | Number of records this record matches deterministically |
+| `z_cluster_count` | Number of records in the cluster this record belongs to |
+| `z_record_centrality` | How central this record is within its cluster: `z_record_edges / (n-1)` |
+| `z_record_determinism` | Proportion of this record's connectivity explained by deterministic rules: `z_record_deterministic_edges / (n-1)` |
 
 ### Reading record statistics
 
diff --git a/docs/platform-guides/platform-guide-for-aws-glue.md b/docs/platform-guides/platform-guide-for-aws-glue.md
index a455afccf..e62577c6b 100644
--- a/docs/platform-guides/platform-guide-for-aws-glue.md
+++ b/docs/platform-guides/platform-guide-for-aws-glue.md
@@ -35,7 +35,14 @@ Create an S3 bucket (for example `zingg-production-storage`) and upload all six
 3. Set the region to match where your Glue jobs will run (for example `us-east-1`).
 4. Once created, upload the following JAR files into a `/jars/` folder:
 
-<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.7.0.jar</code></td><td valign="top">The Zingg engine</td></tr><tr><td valign="top"><code>zingg-common-client-0.7.0.jar</code></td><td valign="top">Common client</td></tr><tr><td valign="top"><code>zingg-common-core-0.7.0.jar</code></td><td valign="top">Common core</td></tr><tr><td valign="top"><code>zingg-spark-client-0.7.0.jar</code></td><td valign="top">Spark client</td></tr><tr><td valign="top"><code>zingg-spark-core-0.7.0.jar</code></td><td valign="top">Spark core</td></tr><tr><td valign="top"><code>zingg-common-infra-0.7.0.jar</code></td><td valign="top">Infrastructure link</td></tr></tbody></table>
+| JAR | Purpose |
+|---|---|
+| `zingg-0.7.0.jar` | The Zingg engine |
+| `zingg-common-client-0.7.0.jar` | Common client |
+| `zingg-common-core-0.7.0.jar` | Common core |
+| `zingg-spark-client-0.7.0.jar` | Spark client |
+| `zingg-spark-core-0.7.0.jar` | Spark core |
+| `zingg-common-infra-0.7.0.jar` | Infrastructure link |
 
 Download all JARs from `github.com/zinggAI/zingg/releases`. Also upload your data file to the bucket root.
 
@@ -55,7 +62,12 @@ AWS Glue Interactive Sessions require a dedicated IAM Role that allows the noteb
 
 Search for and attach these four AWS managed policies:
 
-<table><thead><tr><th valign="top">Policy</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>AmazonS3FullAccess</code></td><td valign="top">Read and write data and models in S3</td></tr><tr><td valign="top"><code>AWSGlueConsoleFullAccess</code></td><td valign="top">Manage Glue jobs via the Studio UI</td></tr><tr><td valign="top"><code>AWSGlueServiceRole</code></td><td valign="top">Required for Glue to run worker nodes</td></tr><tr><td valign="top"><code>AmazonQDeveloperAccess</code></td><td valign="top">Enables AI coding assistance (optional)</td></tr></tbody></table>
+| Policy | Purpose |
+|---|---|
+| `AmazonS3FullAccess` | Read and write data and models in S3 |
+| `AWSGlueConsoleFullAccess` | Manage Glue jobs via the Studio UI |
+| `AWSGlueServiceRole` | Required for Glue to run worker nodes |
+| `AmazonQDeveloperAccess` | Enables AI coding assistance (optional) |
 
 **Step 2c: Attach an inline policy**
 
diff --git a/docs/platform-guides/platform-guide-for-azure-databricks.md b/docs/platform-guides/platform-guide-for-azure-databricks.md
index 79e895570..6cff12158 100644
--- a/docs/platform-guides/platform-guide-for-azure-databricks.md
+++ b/docs/platform-guides/platform-guide-for-azure-databricks.md
@@ -657,7 +657,7 @@ print(outputDF.count())
 **Read more**: Enterprise output includes `ZINGG_ID` — a globally unique, persistent identifier for each resolved entity. Unlike `Z_CLUSTER` in Community, `ZINGG_ID` does not change between runs including incremental runs.
 
 * For output column definitions → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
-* For Zingg ID lifecycle → [Zingg ID](/broken/pages/9QpDFW20AMt0UJ4cEW6b)
+* For Zingg ID lifecycle → [Z Cluster and Zingg ID](../zingg-concepts/z-cluster-and-zingg-id.md)
 {% endhint %}
 
 ### Notebook 06: Run incremental matching
diff --git a/docs/platform-guides/platform-guide-for-gcp-dataproc.md b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
index 0de1680d0..9e6d0d95e 100644
--- a/docs/platform-guides/platform-guide-for-gcp-dataproc.md
+++ b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
@@ -21,7 +21,11 @@ Uses `Arguments`, `FieldDefinition`, `CsvPipe`, and `ZinggWithSpark`. Runs on a
 
 Zingg on GCP requires three JARs to bridge Spark with Google Cloud services. Download these to your local machine before creating the cluster.
 
-<table><thead><tr><th valign="top">JAR</th><th valign="top">Purpose</th><th valign="top">Download</th></tr></thead><tbody><tr><td valign="top"><code>zingg-0.7.0.jar</code></td><td valign="top">The Zingg engine</td><td valign="top"><code>github.com/zinggAI/zingg/releases</code></td></tr><tr><td valign="top"><code>spark-3.5-bigquery-0.44.1.jar</code></td><td valign="top">BigQuery connector</td><td valign="top"><code>github.com/GoogleCloudDataproc/spark-bigquery-connector</code></td></tr><tr><td valign="top"><code>gcs-connector-hadoop3-latest.jar</code></td><td valign="top">GCS connector</td><td valign="top"><code>docs.cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage</code></td></tr></tbody></table>
+| JAR | Purpose | Download |
+|---|---|---|
+| `zingg-0.7.0.jar` | The Zingg engine | `github.com/zinggAI/zingg/releases` |
+| `spark-3.5-bigquery-0.44.1.jar` | BigQuery connector | `github.com/GoogleCloudDataproc/spark-bigquery-connector` |
+| `gcs-connector-hadoop3-latest.jar` | GCS connector | `docs.cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage` |
 
 Create a GCS bucket and upload the JARs and your dataset. You can do this from the Cloud Console or the `gcloud` CLI.
 
diff --git a/docs/platform-guides/platform-guide-for-snowflake.md b/docs/platform-guides/platform-guide-for-snowflake.md
index df60be465..227529b68 100644
--- a/docs/platform-guides/platform-guide-for-snowflake.md
+++ b/docs/platform-guides/platform-guide-for-snowflake.md
@@ -499,6 +499,6 @@ Zingg Enterprise for Snowflake requires a commercial license. To get started:
 
 For detailed API reference, see the [Zingg Python API Documentation](../zingg-python-api/enterprise-zingges-python-api.md).
 
-For the complete step-by-step installation, configuration, and B2B entity resolution guide → [Zingg Enterprise Snowflake Instructions](../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
+For the complete step-by-step installation, configuration, and B2B entity resolution guide → [Zingg Enterprise Snowflake Instructions](../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endtab %}
 {% endtabs %}
\ No newline at end of file
diff --git a/docs/recipes-and-integration/pre-trained-models.md b/docs/recipes-and-integration/pre-trained-models.md
index 8e319838c..149f8ef65 100644
--- a/docs/recipes-and-integration/pre-trained-models.md
+++ b/docs/recipes-and-integration/pre-trained-models.md
@@ -132,6 +132,10 @@ This is faster than building a model from scratch when your data is structurally
 
 Zingg ships with pre-trained models for several datasets. All models are bundled under the `models/` directory in the Zingg installation. Check `models/modelIds.txt` for the complete list of available models and their IDs.
 
-<table><thead><tr><th valign="top">Model</th><th valign="top">Dataset</th><th valign="top">Fields</th></tr></thead><tbody><tr><td valign="top">FEBRL</td><td valign="top">Synthetic Australian population</td><td valign="top"><code>fname</code>, <code>lname</code>, <code>stNo</code>, <code>add1</code>, <code>add2</code>, <code>city</code>, <code>areacode</code>, <code>state</code>, <code>dob</code>, <code>ssn</code></td></tr><tr><td valign="top">North Carolina Voters</td><td valign="top">US voter registration records</td><td valign="top">Name, address, date of birth</td></tr><tr><td valign="top">Amazon-Google Products</td><td valign="top">Product catalog matching</td><td valign="top">Product title, description</td></tr></tbody></table>
+| Model | Dataset | Fields |
+|---|---|---|
+| FEBRL | Synthetic Australian population | `fname`, `lname`, `stNo`, `add1`, `add2`, `city`, `areacode`, `state`, `dob`, `ssn` |
+| North Carolina Voters | US voter registration records | Name, address, date of birth |
+| Amazon-Google Products | Product catalog matching | Product title, description |
 
 For schemas and model IDs, see `models/modelIds.txt` inside your Zingg installation.
diff --git a/docs/reference/cli-command-reference.md b/docs/reference/cli-command-reference.md
index a7fd2ac42..62a6a18c5 100644
--- a/docs/reference/cli-command-reference.md
+++ b/docs/reference/cli-command-reference.md
@@ -18,11 +18,39 @@ Use `--run` to execute a Python program through `zingg.sh`. Zingg Python program
 
 ### All phases
 
-<table><thead><tr><th width="199.75860595703125" valign="top">Phase name</th><th width="119.59686279296875" valign="top">Edition</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>findTrainingData</code></td><td valign="top">All</td><td valign="top">Scans the dataset and selects candidate pairs for labeling.</td></tr><tr><td valign="top"><code>label</code></td><td valign="top">All</td><td valign="top">Opens interactive labeling session. Run after<br><code>findTrainingData</code>.</td></tr><tr><td valign="top"><code>generateDocs</code></td><td valign="top">All</td><td valign="top">Generates HTML documentation of labeled<br>training data for review.</td></tr><tr><td valign="top"><code>train</code></td><td valign="top">All</td><td valign="top">Builds blocking and similarity models using labeled training data.</td></tr><tr><td valign="top"><code>match</code></td><td valign="top">All</td><td valign="top">Applies trained models to the full dataset. Writes clusters to output.</td></tr><tr><td valign="top"><code>link</code></td><td valign="top">All</td><td valign="top">Matches records across two separate datasets. Same model as <code>match</code>.</td></tr><tr><td valign="top"><code>recommend</code></td><td valign="top">All</td><td valign="top">Generates stopword candidates for a specified field.</td></tr><tr><td valign="top"><code>verifyBlocking</code></td><td valign="top">Enterprise</td><td valign="top">Checks blocking model coverage against known<br>matching pairs.</td></tr><tr><td valign="top"><code>trainMatch</code></td><td valign="top">Enterprise</td><td valign="top">Combined <code>train</code> and <code>match</code> in one phase call.</td></tr><tr><td valign="top"><code>findAndLabel</code></td><td valign="top">Enterprise</td><td valign="top">Combined <code>findTrainingData</code> and label in one phase call.</td></tr><tr><td valign="top"><code>runIncremental</code></td><td valign="top">Enterprise</td><td valign="top">Updates identity graph with new or changed<br>records without a full re-match.</td></tr><tr><td valign="top"><code>explainOutput</code></td><td valign="top">Enterprise</td><td valign="top">Shows pair-level evidence for how a specific cluster was formed.</td></tr><tr><td valign="top"><code>generateDocs</code></td><td valign="top">All</td><td valign="top">Generates HTML documentation of model and data statistics.</td></tr><tr><td valign="top"><code>reassignZinggId</code></td><td valign="top">Enterprise Plus</td><td valign="top">Reassigns Zingg IDs when switching<br>to a new trained model.</td></tr><tr><td valign="top"><code>updateLabel</code></td><td valign="top">All</td><td valign="top">Revisit and update previously marked training pairs. Run before <code>train</code> to correct labeling errors. Run <code>generateDocs</code> first to identify pairs to update.</td></tr><tr><td valign="top"><code>diff</code></td><td valign="top">Enterprise</td><td valign="top">Compare two model outputs to understand exactly what changed between them before deploying. Identifies which clusters merged, split, or moved.</td></tr><tr><td valign="top"><code>runLookup</code></td><td valign="top">Enterprise</td><td valign="top">Look up specific records in existing match output to find which entity cluster they belong to and get their Zingg ID</td></tr></tbody></table>
+| Phase name | Edition | Description |
+|---|---|---|
+| `findTrainingData` | All | Scans the dataset and selects candidate pairs for labeling. |
+| `label` | All | Opens interactive labeling session. Run after `findTrainingData`. |
+| `generateDocs` | All | Generates HTML documentation of labeled training data for review. |
+| `train` | All | Builds blocking and similarity models using labeled training data. |
+| `match` | All | Applies trained models to the full dataset. Writes clusters to output. |
+| `link` | All | Matches records across two separate datasets. Same model as `match`. |
+| `recommend` | All | Generates stopword candidates for a specified field. |
+| `verifyBlocking` | Enterprise | Checks blocking model coverage against known matching pairs. |
+| `trainMatch` | Enterprise | Combined `train` and `match` in one phase call. |
+| `findAndLabel` | Enterprise | Combined `findTrainingData` and label in one phase call. |
+| `runIncremental` | Enterprise | Updates identity graph with new or changed records without a full re-match. |
+| `explainOutput` | Enterprise | Shows pair-level evidence for how a specific cluster was formed. |
+| `generateDocs` | All | Generates HTML documentation of model and data statistics. |
+| `reassignZinggId` | Enterprise Plus | Reassigns Zingg IDs when switching to a new trained model. |
+| `updateLabel` | All | Revisit and update previously marked training pairs. Run before `train` to correct labeling errors. Run `generateDocs` first to identify pairs to update. |
+| `diff` | Enterprise | Compare two model outputs to understand exactly what changed between them before deploying. Identifies which clusters merged, split, or moved. |
+| `runLookup` | Enterprise | Look up specific records in existing match output to find which entity cluster they belong to and get their Zingg ID |
 
 ### All flags
 
-<table><thead><tr><th width="222.57421875" valign="top">Flag</th><th width="171.83984375" valign="top">Required/Optional</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>--phase</code></td><td valign="top">Required</td><td valign="top">The Zingg phase is to run. See phases table above.</td></tr><tr><td valign="top"><code>--conf</code></td><td valign="top">Required</td><td valign="top">Path to your JSON config file.</td></tr><tr><td valign="top"><code>--properties-file</code></td><td valign="top">Optional</td><td valign="top">Path to Zingg runtime properties<br>file (<code>zingg.conf</code>). This is required for Snowflake and some cloud connectors.</td></tr><tr><td valign="top"><code>--zinggDir</code></td><td valign="top">Optional</td><td valign="top">Override the <code>zinggDir</code> from config. Useful<br>for S3 and GCS paths.</td></tr><tr><td valign="top"><code>--showConcise</code></td><td valign="top">Optional</td><td valign="top"><code>true</code> or <code>false</code>. When <code>true</code>, hides<br><code>DONT_USE</code> fields in the label phase<br>terminal display.</td></tr><tr><td valign="top"><code>--column</code></td><td valign="top">Required with <code>recommend</code></td><td valign="top">The field name to generate stopword<br>recommendations for.</td></tr><tr><td valign="top"><code>--originalZinggId</code></td><td valign="top">Required with<br><code>reassignZinggId</code></td><td valign="top">Path to the original production config file.</td></tr><tr><td valign="top"><code>--run</code></td><td valign="top">Optional</td><td valign="top">Path to a Python program to execute through <code>zingg.sh</code>. Use instead of <code>--phase</code> and <code>--conf</code> when running Python-based Zingg programs.</td></tr><tr><td valign="top"><code>--zinggid</code></td><td valign="top">Required with <code>explainOutput</code></td><td valign="top">The Zingg ID of the cluster to explain. Find Zingg IDs in your match output <code>ZINGG_ID</code> column.</td></tr></tbody></table>
+| Flag | Required/Optional | Description |
+|---|---|---|
+| `--phase` | Required | The Zingg phase is to run. See phases table above. |
+| `--conf` | Required | Path to your JSON config file. |
+| `--properties-file` | Optional | Path to Zingg runtime properties file (`zingg.conf`). This is required for Snowflake and some cloud connectors. |
+| `--zinggDir` | Optional | Override the `zinggDir` from config. Useful for S3 and GCS paths. |
+| `--showConcise` | Optional | `true` or `false`. When `true`, hides `DONT_USE` fields in the label phase terminal display. |
+| `--column` | Required with `recommend` | The field name to generate stopword recommendations for. |
+| `--originalZinggId` | Required with `reassignZinggId` | Path to the original production config file. |
+| `--run` | Optional | Path to a Python program to execute through `zingg.sh`. Use instead of `--phase` and `--conf` when running Python-based Zingg programs. |
+| `--zinggid` | Required with `explainOutput` | The Zingg ID of the cluster to explain. Find Zingg IDs in your match output `ZINGG_ID` column. |
 
 ### Example commands
 
diff --git a/docs/reference/configuration-schema.md b/docs/reference/configuration-schema.md
index b7014ab1d..d40fcc327 100644
--- a/docs/reference/configuration-schema.md
+++ b/docs/reference/configuration-schema.md
@@ -14,23 +14,50 @@ This page is the reference. Every Zingg configuration parameter, JSON key and Py
 
 ### Top-level parameters
 
-<table><thead><tr><th width="150.62890625" valign="top">Parameter</th><th width="108.40234375" valign="top">Type</th><th width="113.53912353515625" valign="top">Edition</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>modelId</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Unique identifier for this model. Used as folder name under <code>zinggDir</code>. Use the same <code>modelId</code> across all phases for a given run.</td></tr><tr><td valign="top"><code>zinggDir</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Root directory where Zingg writes model files and training data. Can be a local path, DBFS path (<code>dbfs:/</code>), GCS path (<code>gs://</code>), S3 path (<code>s3a://</code>), or OneLake path (<code>abfss://</code>).</td></tr><tr><td valign="top"><code>numPartitions</code></td><td valign="top">integer</td><td valign="top">All editions</td><td valign="top">Number of Spark partitions. Controls how data is distributed across cluster nodes. Rule of thumb: set to approximately 20–30× the number of worker vCPUs. Start with 4–8 for a standard development cluster. Increase proportionally for larger datasets.</td></tr><tr><td valign="top"><code>labelDataSampleSize</code></td><td valign="top">float (0.0001–0.1)</td><td valign="top">All editions</td><td valign="top">Fraction of the dataset scanned when running <code>findTrainingData</code>. Valid range: 0.0001 to 0.1. For 100k records use 0.1–0.5. For 1M+ records use 0.01–0.05. Reduce to 0.05 or lower if <code>findTrainingData</code> is slow on large datasets.</td></tr><tr><td valign="top"><code>stopWordsCutoff</code></td><td valign="top">float (0–1)</td><td valign="top">All editions, optional</td><td valign="top">Used with the <code>recommend</code> phase. Fraction of high-frequency words to extract as stopword candidates. Default is <code>0.1</code> (10%).</td></tr><tr><td valign="top"><code>collectMetrics</code></td><td valign="top">boolean</td><td valign="top">All editions</td><td valign="top">Controls telemetry collection. Default <code>true</code>. Set to <code>false</code> to disable. When enabled, Zingg captures runtime metrics (record count, field count, running phase, execution time). No input data or user data is ever captured. See <a href="../security-and-privacy/security-and-privacy.md">Security and Privacy</a> for full details.</td></tr><tr><td valign="top"><code>passthroughExpr</code></td><td valign="top">string</td><td valign="top"><strong>Enterprise only</strong>, optional</td><td valign="top">SQL expression defining records to exclude from matching. Records matching this expression are carried through to output with their own Zingg ID but do not influence cluster formation. Example: <code>"fname = 'matilda'"</code>.</td></tr><tr><td valign="top"><code>deterministicMatching</code></td><td valign="top">array</td><td valign="top"><strong>Enterprise only</strong>, optional</td><td valign="top">Deterministic match conditions, where exact field matches always result in a match regardless of the probabilistic score. See <a href="../zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md">Deterministic vs Probabilistic Matching</a> for full configuration.</td></tr><tr><td valign="top"><code>setBlockingModel</code></td><td valign="top">string</td><td valign="top"><strong>Enterprise only</strong>, optional</td><td valign="top">Blocking strategy. Valid values: <code>"DEFAULT"</code>, <code>"WIDER"</code>. If not set, the model uses <code>DEFAULT</code>.</td></tr></tbody></table>
+| Parameter | Type | Edition | Description |
+|---|---|---|---|
+| `modelId` | string | All editions | Unique identifier for this model. Used as folder name under `zinggDir`. Use the same `modelId` across all phases for a given run. |
+| `zinggDir` | string | All editions | Root directory where Zingg writes model files and training data. Can be a local path, DBFS path (`dbfs:/`), GCS path (`gs://`), S3 path (`s3a://`), or OneLake path (`abfss://`). |
+| `numPartitions` | integer | All editions | Number of Spark partitions. Controls how data is distributed across cluster nodes. Rule of thumb: set to approximately 20–30× the number of worker vCPUs. Start with 4–8 for a standard development cluster. Increase proportionally for larger datasets. |
+| `labelDataSampleSize` | float (0.0001–0.1) | All editions | Fraction of the dataset scanned when running `findTrainingData`. Valid range: 0.0001 to 0.1. For 100k records use 0.1–0.5. For 1M+ records use 0.01–0.05. Reduce to 0.05 or lower if `findTrainingData` is slow on large datasets. |
+| `stopWordsCutoff` | float (0–1) | All editions, optional | Used with the `recommend` phase. Fraction of high-frequency words to extract as stopword candidates. Default is `0.1` (10%). |
+| `collectMetrics` | boolean | All editions | Controls telemetry collection. Default `true`. Set to `false` to disable. When enabled, Zingg captures runtime metrics (record count, field count, running phase, execution time). No input data or user data is ever captured. See [Security and Privacy](../security-and-privacy/security-and-privacy.md) for full details. |
+| `passthroughExpr` | string | **Enterprise only**, optional | SQL expression defining records to exclude from matching. Records matching this expression are carried through to output with their own Zingg ID but do not influence cluster formation. Example: `"fname = 'matilda'"`. |
+| `deterministicMatching` | array | **Enterprise only**, optional | Deterministic match conditions, where exact field matches always result in a match regardless of the probabilistic score. See [Deterministic vs Probabilistic Matching](../zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md) for full configuration. |
+| `setBlockingModel` | string | **Enterprise only**, optional | Blocking strategy. Valid values: `"DEFAULT"`, `"WIDER"`. If not set, the model uses `DEFAULT`. |
 
 ### `fieldDefinition`
 
 Each entry in the `fieldDefinition` array defines one field in your input schema.
 
-<table><thead><tr><th width="166.0765380859375" valign="top">Parameter</th><th width="116.16015625" valign="top">Type</th><th width="113.979736328125" valign="top">Edition</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>fieldName</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Name of the field in your input dataset. Must match exactly.</td></tr><tr><td valign="top"><code>matchType</code></td><td valign="top">enum</td><td valign="top">All editions</td><td valign="top">How to compare this field. See <a href="../zingg-concepts/zingg-configuration/field-definition/match-types/">Match Types</a> for all values.</td></tr><tr><td valign="top"><code>fields</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">The field to use for comparison. Keep the same as <code>fieldName</code> for standard use. Can reference a different column name if the field you want to compare appears under a different name in the input schema. For now, keep this the same as <code>fieldName</code> unless specifically advised otherwise.</td></tr><tr><td valign="top"><code>dataType</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Spark SQL data type - <code>string</code>, <code>integer</code>, <code>double</code>, <code>date</code>, <code>timestamp</code>.</td></tr><tr><td valign="top"><code>stopWords</code></td><td valign="top">string (path)</td><td valign="top">All editions, optional</td><td valign="top">Path to a CSV file of stopwords for this field. One word per row. The file must have a header row. Generated by the <code>recommend</code> phase.</td></tr><tr><td valign="top"><code>primaryKey</code></td><td valign="top">boolean</td><td valign="top"><strong>Enterprise only</strong>, optional</td><td valign="top">Marks this field as the primary key. Used by <code>runIncremental</code> to identify records uniquely across runs and by <code>reassignZinggId</code> for ID preservation. Set <code>true</code> on exactly one field per config.</td></tr><tr><td valign="top"><code>postProcessors</code></td><td valign="top">string</td><td valign="top"><strong>Enterprise only</strong>, optional</td><td valign="top">Standardise postprocessor reference. Format: <code>STANDARDISE_&#x3C;basename></code> where <code>&#x3C;basename>.json</code> is the mapping file. Normalises field values to canonical form after matching.</td></tr></tbody></table>
+| Parameter | Type | Edition | Description |
+|---|---|---|---|
+| `fieldName` | string | All editions | Name of the field in your input dataset. Must match exactly. |
+| `matchType` | enum | All editions | How to compare this field. See [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/) for all values. |
+| `fields` | string | All editions | The field to use for comparison. Keep the same as `fieldName` for standard use. Can reference a different column name if the field you want to compare appears under a different name in the input schema. For now, keep this the same as `fieldName` unless specifically advised otherwise. |
+| `dataType` | string | All editions | Spark SQL data type - `string`, `integer`, `double`, `date`, `timestamp`. |
+| `stopWords` | string (path) | All editions, optional | Path to a CSV file of stopwords for this field. One word per row. The file must have a header row. Generated by the `recommend` phase. |
+| `primaryKey` | boolean | **Enterprise only**, optional | Marks this field as the primary key. Used by `runIncremental` to identify records uniquely across runs and by `reassignZinggId` for ID preservation. Set `true` on exactly one field per config. |
+| `postProcessors` | string | **Enterprise only**, optional | Standardise postprocessor reference. Format: `STANDARDISE_<basename>` where `<basename>.json` is the mapping file. Normalises field values to canonical form after matching. |
 
 ### `data/output` (pipe)
 
-<table><thead><tr><th width="119.6796875" valign="top">Parameter</th><th width="110.30078125" valign="top">Type</th><th width="113.47186279296875" valign="top">Edition</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>name</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Logical name for this pipe. Used in logging.</td></tr><tr><td valign="top"><code>format</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Connector format. See <a href="../connect-your-data/pipes-and-data-connections.md">Connect Your Data</a> for all supported formats.</td></tr><tr><td valign="top"><code>props</code></td><td valign="top">object</td><td valign="top">All editions</td><td valign="top">Connector-specific properties. Keys depend on the format. See <a href="../connect-your-data/pipes-and-data-connections.md">Connect Your Data</a> for <code>props</code> per connector.</td></tr><tr><td valign="top"><code>schema</code></td><td valign="top">string</td><td valign="top">All editions</td><td valign="top">Spark SQL schema string. Required for CSV. Format: <code>"field1 type1, field2 type2, ..."</code>.</td></tr></tbody></table>
+| Parameter | Type | Edition | Description |
+|---|---|---|---|
+| `name` | string | All editions | Logical name for this pipe. Used in logging. |
+| `format` | string | All editions | Connector format. See [Connect Your Data](../connect-your-data/pipes-and-data-connections.md) for all supported formats. |
+| `props` | object | All editions | Connector-specific properties. Keys depend on the format. See [Connect Your Data](../connect-your-data/pipes-and-data-connections.md) for `props` per connector. |
+| `schema` | string | All editions | Spark SQL schema string. Required for CSV. Format: `"field1 type1, field2 type2, ..."`. |
 
 ### `outputStats` (statistics pipe)
 
 Uses the same `format`, `name`, and `props` pattern as `data` and `output`. The path must contain the `$ZINGG_DYNAMIC_STAT_NAME` placeholder, which is replaced at runtime with `SUMMARY`, `CLUSTER`, or `RECORD` depending on the statistics type written. If not configured, statistics are not written and the run proceeds normally.
 
-<table><thead><tr><th width="164.5" valign="top">Parameter</th><th width="102.40234375" valign="top">Type</th><th width="172.96484375" valign="top">Edition</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>name</code></td><td valign="top">string</td><td valign="top">Enterprise only</td><td valign="top">Logical name for this stats pipe.</td></tr><tr><td valign="top"><code>format</code></td><td valign="top">string</td><td valign="top">Enterprise only</td><td valign="top">Connector format. Usually <code>csv</code>.</td></tr><tr><td valign="top"><code>props.location</code></td><td valign="top">string</td><td valign="top">Enterprise only</td><td valign="top">Path must contain <code>$ZINGG_DYNAMIC_STAT_NAME</code>. Example: <code>/tmp/zinggStats_$ZINGG_DYNAMIC_STAT_NAME</code></td></tr></tbody></table>
+| Parameter | Type | Edition | Description |
+|---|---|---|---|
+| `name` | string | Enterprise only | Logical name for this stats pipe. |
+| `format` | string | Enterprise only | Connector format. Usually `csv`. |
+| `props.location` | string | Enterprise only | Path must contain `$ZINGG_DYNAMIC_STAT_NAME`. Example: `/tmp/zinggStats_$ZINGG_DYNAMIC_STAT_NAME` |
 
 ### JSON Config
 
@@ -75,7 +102,22 @@ Environment variable substitution applies anywhere in the JSON config - top-leve
 
 For every JSON parameter, there is an equivalent Python API method on the `Arguments` (Community) or `EArguments` (Enterprise) object.
 
-<table><thead><tr><th valign="top">JSON parameter</th><th valign="top">Python API method</th><th width="145.0078125" valign="top">Edition</th><th valign="top">Example</th></tr></thead><tbody><tr><td valign="top"><code>modelId</code></td><td valign="top"><code>args.setModelId()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setModelId("100")</code></td></tr><tr><td valign="top"><code>zinggDir</code></td><td valign="top"><code>args.setZinggDir()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setZinggDir("/tmp/models")</code></td></tr><tr><td valign="top"><code>numPartitions</code></td><td valign="top"><code>args.setNumPartitions()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setNumPartitions(4)</code></td></tr><tr><td valign="top"><code>labelDataSampleSize</code></td><td valign="top"><code>args.setLabelDataSampleSize()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setLabelDataSampleSize(0.5)</code></td></tr><tr><td valign="top"><code>fieldDefinition</code></td><td valign="top"><code>args.setFieldDefinition([])</code></td><td valign="top">All editions</td><td valign="top"><code>args.setFieldDefinition(fieldDefs)</code></td></tr><tr><td valign="top"><code>data</code></td><td valign="top"><code>args.setData()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setData(inputPipe)</code></td></tr><tr><td valign="top"><code>output</code></td><td valign="top"><code>args.setOutput()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setOutput(outputPipe)</code></td></tr><tr><td valign="top"><code>collectMetrics</code></td><td valign="top"><code>args.setCollectMetrics()</code></td><td valign="top">All editions</td><td valign="top"><code>args.setCollectMetrics(False)</code></td></tr><tr><td valign="top"><code>outputStats</code></td><td valign="top"><code>args.setOutputStats()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>args.setOutputStats(statsPipe)</code></td></tr><tr><td valign="top"><code>passthroughExpr</code></td><td valign="top"><code>args.setPassthroughExpr()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>args.setPassthroughExpr("fname = 'matilda'")</code></td></tr><tr><td valign="top"><code>deterministicMatching</code></td><td valign="top"><code>args.setDeterministicMatchingCondition()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>args.setDeterministicMatchingCondition(dm1, dm2)</code></td></tr><tr><td valign="top"><code>setBlockingModel</code></td><td valign="top"><code>args.setBlockingModel()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>args.setBlockingModel("DEFAULT")</code></td></tr><tr><td valign="top"><code>primaryKey</code> (field-level)</td><td valign="top"><code>fieldDef.setPrimaryKey()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>recId.setPrimaryKey(True)</code></td></tr><tr><td valign="top"><code>postProcessors</code> (field-level)</td><td valign="top"><code>fieldDef.setPostProcessors()</code></td><td valign="top">Enterprise only</td><td valign="top"><code>job_title.setPostProcessors([StandardisePostprocessorType("STANDARDISE", "jobtitles")])</code></td></tr></tbody></table>
+| JSON parameter | Python API method | Edition | Example |
+|---|---|---|---|
+| `modelId` | `args.setModelId()` | All editions | `args.setModelId("100")` |
+| `zinggDir` | `args.setZinggDir()` | All editions | `args.setZinggDir("/tmp/models")` |
+| `numPartitions` | `args.setNumPartitions()` | All editions | `args.setNumPartitions(4)` |
+| `labelDataSampleSize` | `args.setLabelDataSampleSize()` | All editions | `args.setLabelDataSampleSize(0.5)` |
+| `fieldDefinition` | `args.setFieldDefinition([])` | All editions | `args.setFieldDefinition(fieldDefs)` |
+| `data` | `args.setData()` | All editions | `args.setData(inputPipe)` |
+| `output` | `args.setOutput()` | All editions | `args.setOutput(outputPipe)` |
+| `collectMetrics` | `args.setCollectMetrics()` | All editions | `args.setCollectMetrics(False)` |
+| `outputStats` | `args.setOutputStats()` | Enterprise only | `args.setOutputStats(statsPipe)` |
+| `passthroughExpr` | `args.setPassthroughExpr()` | Enterprise only | `args.setPassthroughExpr("fname = 'matilda'")` |
+| `deterministicMatching` | `args.setDeterministicMatchingCondition()` | Enterprise only | `args.setDeterministicMatchingCondition(dm1, dm2)` |
+| `setBlockingModel` | `args.setBlockingModel()` | Enterprise only | `args.setBlockingModel("DEFAULT")` |
+| `primaryKey` (field-level) | `fieldDef.setPrimaryKey()` | Enterprise only | `recId.setPrimaryKey(True)` |
+| `postProcessors` (field-level) | `fieldDef.setPostProcessors()` | Enterprise only | `job_title.setPostProcessors([StandardisePostprocessorType("STANDARDISE", "jobtitles")])` |
 
 {% hint style="success" icon="right-long" %}
 Sample config files:
diff --git a/docs/reference/hardware-sizing-and-benchmarks.md b/docs/reference/hardware-sizing-and-benchmarks.md
index afee7a973..dd03eb2db 100644
--- a/docs/reference/hardware-sizing-and-benchmarks.md
+++ b/docs/reference/hardware-sizing-and-benchmarks.md
@@ -18,7 +18,12 @@ Use the benchmarks below as starting points when sizing your Zingg deployment. Y
 
 Real-world Zingg runs across different dataset sizes and hardware:
 
-<table><thead><tr><th width="171.26953125" valign="top">Records</th><th>Reported By</th><th width="154.53515625" valign="top">Fields</th><th valign="top">Hardware</th><th valign="top">Runtime</th></tr></thead><tbody><tr><td valign="top">120k</td><td>Zingg Team</td><td valign="top">febrl test schema</td><td valign="top">4 cores, 10 GB RAM, local Spark</td><td valign="top">5 minutes</td></tr><tr><td valign="top">5m (North Carolina Voters)</td><td>Zingg Team</td><td valign="top">voter schema</td><td valign="top">4 cores, 10 GB RAM, local Spark</td><td valign="top">~4 hours</td></tr><tr><td valign="top">9m</td><td>Zingg Team</td><td valign="top">3 fields (first name, last name, email)</td><td valign="top">AWS m5.24xlarge - 96 cores, 384 GB RAM</td><td valign="top">45 minutes</td></tr><tr><td valign="top">80m</td><td>Community User</td><td valign="top">8 to 10 fields</td><td valign="top">1 driver (128 GB RAM, 32 cores) + 8 workers (224 GB RAM, 64 cores)</td><td valign="top">Less than 2 hours</td></tr></tbody></table>
+| Records | Reported By | Fields | Hardware | Runtime |
+|---|---|---|---|---|
+| 120k | Zingg Team | febrl test schema | 4 cores, 10 GB RAM, local Spark | 5 minutes |
+| 5m (North Carolina Voters) | Zingg Team | voter schema | 4 cores, 10 GB RAM, local Spark | ~4 hours |
+| 9m | Zingg Team | 3 fields (first name, last name, email) | AWS m5.24xlarge - 96 cores, 384 GB RAM | 45 minutes |
+| 80m | Community User | 8 to 10 fields | 1 driver (128 GB RAM, 32 cores) + 8 workers (224 GB RAM, 64 cores) | Less than 2 hours |
 
 ### When to use Spark local mode
 
diff --git a/docs/reference/runtime-properties.md b/docs/reference/runtime-properties.md
index de8365215..1df0e6d54 100644
--- a/docs/reference/runtime-properties.md
+++ b/docs/reference/runtime-properties.md
@@ -17,7 +17,12 @@ spark.driver.memory=4g
 spark.sql.shuffle.partitions=200
 ```
 
-<table><thead><tr><th width="309.85546875" valign="top">Property</th><th width="117.21484375" valign="top">Default</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top"><code>spark.master</code></td><td valign="top"><code>local[*]</code></td><td valign="top">Spark master URL. Use <code>local[*]</code> for local development (all cores). For cluster mode, set to your cluster manager URL. On managed platforms (Databricks, Fabric, EMR) this is managed automatically; do not override.</td></tr><tr><td valign="top"><code>spark.executor.memory</code></td><td valign="top"><code>4g</code></td><td valign="top">Memory allocated per executor. Increase if you see OOM errors during training or matching. For large datasets (1M+ records) try <code>8g</code> or <code>16g</code>.</td></tr><tr><td valign="top"><code>spark.driver.memory</code></td><td valign="top"><code>4g</code></td><td valign="top">Memory allocated to the Spark driver. Usually matches or is slightly lower than <code>spark.executor.memory</code>.</td></tr><tr><td valign="top"><code>spark.sql.shuffle.partitions</code></td><td valign="top"><code>200</code></td><td valign="top">Number of partitions used during shuffle operations. Increase for very large datasets during the <code>match</code> phase. A common value for large datasets is <code>400</code>–<code>800</code>.</td></tr></tbody></table>
+| Property | Default | Description |
+|---|---|---|
+| `spark.master` | `local[*]` | Spark master URL. Use `local[*]` for local development (all cores). For cluster mode, set to your cluster manager URL. On managed platforms (Databricks, Fabric, EMR) this is managed automatically; do not override. |
+| `spark.executor.memory` | `4g` | Memory allocated per executor. Increase if you see OOM errors during training or matching. For large datasets (1M+ records) try `8g` or `16g`. |
+| `spark.driver.memory` | `4g` | Memory allocated to the Spark driver. Usually matches or is slightly lower than `spark.executor.memory`. |
+| `spark.sql.shuffle.partitions` | `200` | Number of partitions used during shuffle operations. Increase for very large datasets during the `match` phase. A common value for large datasets is `400`–`800`. |
 
 ### Adding JARs for connectors
 
diff --git a/docs/relations.md b/docs/relations.md
index 8d995d3a8..5e36861c2 100644
--- a/docs/relations.md
+++ b/docs/relations.md
@@ -9,9 +9,9 @@ description: When a single match model is not sufficient
 
 [Zingg Enterprise Feature](#user-content-fn-1)[^1]
 
-In many cases, we want to build the identity graph using a combination of different datasets, schemas and matching logic. An example could be having a source system which only contains userids and emails, another one wtih user name and phone numbers and a few others with person information with addresses. Another example could be some systems capturing spousal information,  but others to be matched on the basis of lastname and address.&#x20;
+In many cases, we want to build the identity graph using a combination of different datasets, schemas and matching logic. An example could be having a source system which only contains userids and emails, another one wtih user name and phone numbers and a few others with person information with addresses. Another example could be some systems capturing spousal information,  but others to be matched on the basis of lastname and address. 
 
-In such cases, Zingg can build the entire graph and relate different models together. In the following case, results of a query with exact match on family Id and a matching model(household) using address and lastname are brought together.&#x20;
+In such cases, Zingg can build the entire graph and relate different models together. In the following case, results of a query with exact match on family Id and a matching model(household) using address and lastname are brought together. 
 
 ````json
 ```
diff --git a/docs/running-zingg/build-and-save-the-model.md b/docs/running-zingg/build-and-save-the-model.md
index 1929fe194..c5752a736 100644
--- a/docs/running-zingg/build-and-save-the-model.md
+++ b/docs/running-zingg/build-and-save-the-model.md
@@ -8,9 +8,13 @@ description: >-
 
 The training phase builds up the Zingg models using the training data from your label sessions and writes them to `zinggDir/modelId` as specified in your config.
 
-<table><thead><tr><th width="205.12890625" valign="top">Model</th><th valign="top">Path</th></tr></thead><tbody><tr><td valign="top">Blocking model</td><td valign="top"><code>zinggDir/modelId/model/block/</code></td></tr><tr><td valign="top">Similarity model</td><td valign="top"><code>zinggDir/modelId/model/zingg.block</code></td></tr><tr><td valign="top">Both models</td><td valign="top">Written during the train phase. Present before any match, link,<br>or incremental run can proceed.</td></tr></tbody></table>
+| Model | Path |
+|---|---|
+| Blocking model | `zinggDir/modelId/model/block/` |
+| Similarity model | `zinggDir/modelId/model/zingg.block` |
+| Both models | Written during the train phase. Present before any match, link, or incremental run can proceed. |
 
-Once saved, reuse the same `modelId` in all subsequent phases - `match`, `link`, and `runIncremental` to apply the trained models to your data.&#x20;
+Once saved, reuse the same `modelId` in all subsequent phases - `match`, `link`, and `runIncremental` to apply the trained models to your data. 
 
 If you retrain with a new model, use `Compare Model Results (diff phase)` in Enterprise to benchmark the new model against the current one before deploying. Zingg shows you exactly which clusters changed, merged, or split between the two models.
 
diff --git a/docs/running-zingg/compare-model-results.md b/docs/running-zingg/compare-model-results.md
index 7cb6acf83..9e3a73ff1 100644
--- a/docs/running-zingg/compare-model-results.md
+++ b/docs/running-zingg/compare-model-results.md
@@ -113,8 +113,10 @@ zinggDiff.initAndExecute()
 ```
 {% endtab %}
 
-{% tab title="Enterprise for Snowflake" %}
-_**Enterprise Snowflake content for this topic to be provided by Sonal**_
+{% tab title="Enterprise Snowflake" %}
+{% hint style="info" %}
+**Coming soon** — Enterprise Snowflake guidance for comparing model results is in progress and will be published here when available.
+{% endhint %}
 {% endtab %}
 {% endtabs %}
 
diff --git a/docs/running-zingg/create-training-data/verify-blocking.md b/docs/running-zingg/create-training-data/verify-blocking.md
index 8b3b8c721..ca9230f35 100644
--- a/docs/running-zingg/create-training-data/verify-blocking.md
+++ b/docs/running-zingg/create-training-data/verify-blocking.md
@@ -92,7 +92,7 @@ influence matching.
 * [Label Training Pairs](label-training-pairs.md) - how to add more training data
 * [Configure Zingg](../configure-zingg.md) - changing field match types
 * [Blocking Strategies: DEFAULT vs WIDER](../../tuning/blocking-strategy.md) - reorder candidate fields in the blocking tree (Enterprise only)
-* [Custom Blocking and Similarity Functions](../../reference/custom-blocking-similarity.md) Functions
+* [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md) - define custom blocking functions
 {% endhint %}
 
 {% hint style="success" icon="right-long" %}
@@ -172,10 +172,10 @@ If `verifyBlocking` shows that many known matching pairs are not being blocked t
 3. **Try the `WIDER` blocking strategy** (Enterprise only) if large blocks suggest the tree is over-relying on one or two fields → [Blocking Strategies: DEFAULT vs WIDER](../../tuning/blocking-strategy.md)
 
 {% hint style="success" icon="right-long" %}
-**Consider custom blocking functions** for specialised data patterns → [Custom Blocking and Similarity](/broken/pages/DWdyf7az3MmhJaVca3k7)
+**Consider custom blocking functions** for specialised data patterns → [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
 
 **Read more**:
 
 * Blocking model concept and how it fits in the pipeline → [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
-* Custom blocking functions for advanced tuning → [Custom Blocking and Similarity Functions](../../reference/custom-blocking-similarity.md)
+* Custom blocking functions for advanced tuning → [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
 {% endhint %}
diff --git a/docs/running-zingg/install-zingg.md b/docs/running-zingg/install-zingg.md
index 3c57f6ad7..baa6435cc 100644
--- a/docs/running-zingg/install-zingg.md
+++ b/docs/running-zingg/install-zingg.md
@@ -344,7 +344,7 @@ cd zingg
 This builds Zingg models and finds duplicates in `examples/febrl/test.csv`. You will see Zingg logs on the console and output files under `/tmp/zinggOutput` with matching records sharing the same cluster ID. If you see this, Zingg is correctly installed.
 {% endtab %}
 
-{% tab title="Snowflake (ENT)" %}
+{% tab title="Enterprise Snowflake" %}
 {% hint style="info" icon="right-long" %}
 Enterprise only. Zingg on Snowflake uses the Enterprise Snowflake package and runs natively inside Snowflake using Snowpark — no Spark cluster required.
 {% endhint %}
@@ -393,7 +393,7 @@ Add `ZINGG_SNOW_JAR` and `ZINGG_SNOW_HOME` to `.bashrc` so they persist across s
 
 For the full end-to-end Snowflake guide → [Run on Snowflake](../platform-guides/platform-guide-for-snowflake.md)
 
-For complete step-by-step instructions with B2B examples, SQL/Python APIs, and incremental matching → [Zingg Enterprise Snowflake Instructions](../../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
+For complete step-by-step instructions with B2B examples, SQL/Python APIs, and incremental matching → [Zingg Enterprise Snowflake Instructions](../stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md)
 {% endhint %}
 
 ### **Create the Snowflake properties file (`snowEnv.txt`)**
diff --git a/docs/running-zingg/set-deterministic-matching-conditions.md b/docs/running-zingg/set-deterministic-matching-conditions.md
index 1272c8239..fcbc69a5a 100644
--- a/docs/running-zingg/set-deterministic-matching-conditions.md
+++ b/docs/running-zingg/set-deterministic-matching-conditions.md
@@ -19,7 +19,11 @@ Each `matchCondition` is evaluated independently. Any pair that satisfies at lea
 
 Using the example above:
 
-<table><thead><tr><th width="252.984375" valign="top">Condition</th><th valign="top">What it means</th></tr></thead><tbody><tr><td valign="top"><code>fname</code> + <code>stNo</code> + <code>add1</code> match <code>exactly</code></td><td valign="top">Same first name at the same street number and address line → treated as same entity, score 1.</td></tr><tr><td valign="top"><code>fname</code> + <code>dob</code> + <code>ssn</code> match <code>exactly</code></td><td valign="top">Same first name, date of birth, and SSN → treated as same entity, score 1.</td></tr><tr><td valign="top"><code>fname</code> + <code>email</code> match <code>exactly</code></td><td valign="top">Same first name and email address → treated as same entity, score 1.</td></tr></tbody></table>
+| Condition | What it means |
+|---|---|
+| `fname` + `stNo` + `add1` match `exactly` | Same first name at the same street number and address line → treated as same entity, score 1. |
+| `fname` + `dob` + `ssn` match `exactly` | Same first name, date of birth, and SSN → treated as same entity, score 1. |
+| `fname` + `email` match `exactly` | Same first name and email address → treated as same entity, score 1. |
 
 <details>
 
diff --git a/docs/running-zingg/step-by-step-guide.md b/docs/running-zingg/step-by-step-guide.md
index e351b5d0f..18bed4d81 100644
--- a/docs/running-zingg/step-by-step-guide.md
+++ b/docs/running-zingg/step-by-step-guide.md
@@ -70,7 +70,7 @@ Run `findTrainingData` to generate candidate pairs, then label each pair as Matc
 {% step %}
 ### Verify Blocking (Enterprise Only)
 
-Run `verifyBlocking` to check what percentage of your known matching pairs are being blocked together correctly. Run after labelling and before committing to a full training run. If coverage is low, return to labelling.&#x20;
+Run `verifyBlocking` to check what percentage of your known matching pairs are being blocked together correctly. Run after labelling and before committing to a full training run. If coverage is low, return to labelling. 
 
 {% hint style="success" icon="right-long" %}
 **Read more**: [Verify Blocking](create-training-data/verify-blocking.md)
@@ -80,7 +80,7 @@ Run `verifyBlocking` to check what percentage of your known matching pairs are b
 {% step %}
 ### Verify Model (Optional)
 
-Run `generateDocs` to produce a human-readable HTML report of your training data, including pairs labeled as matches and non-matches. Useful for sharing with subject-matter experts for feedback before training. Run `updateLabel` if needed to incorporate SME comments.&#x20;
+Run `generateDocs` to produce a human-readable HTML report of your training data, including pairs labeled as matches and non-matches. Useful for sharing with subject-matter experts for feedback before training. Run `updateLabel` if needed to incorporate SME comments. 
 
 {% hint style="success" icon="right-long" %}
 **Read more**: [Generate Model Documentation](generate-model-documentation.md)
@@ -116,11 +116,11 @@ Apply the trained model to your data:
 {% endstep %}
 
 {% step %}
-### Interpret Output&#x20;
+### Interpret Output 
 
 Review output scores to understand match quality. Use `Z_MINSCORE` and `Z_MAXSCORE` to set confidence thresholds for automated vs human-reviewed clusters.
 
-Run `explain` to understand cluster formation and check the output statistics (Enterprise Only) to understand and explain matches.&#x20;
+Run `explain` to understand cluster formation and check the output statistics (Enterprise Only) to understand and explain matches. 
 
 {% hint style="success" icon="right-long" %}
 **Read more**: [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
@@ -130,9 +130,9 @@ Run `explain` to understand cluster formation and check the output statistics (E
 {% step %}
 ### Tune (if needed)
 
-If match results need improvement, return to find-and-label with focused training data for the patterns being missed, then retrain. Remove stopwords from fields like addresses and company names to improve blocking.&#x20;
+If match results need improvement, return to find-and-label with focused training data for the patterns being missed, then retrain. Remove stopwords from fields like addresses and company names to improve blocking. 
 
-If the Zingg job is slow, add more labels. The blocking model learns from the labels and adding more matches will help it branch better.&#x20;
+If the Zingg job is slow, add more labels. The blocking model learns from the labels and adding more matches will help it branch better. 
 
 {% hint style="success" icon="right-long" %}
 **Read more**: [Improve Accuracy](../tuning/improve-accuracy/)
diff --git a/docs/scoring.md b/docs/scoring.md
index b54adcdfb..1f3cec7bb 100644
--- a/docs/scoring.md
+++ b/docs/scoring.md
@@ -22,5 +22,5 @@ The threshold is automatically chosen so that you can pick up most of the result
 A few things to keep in mind while interpreting the scores:
 
 * Matching is _transitive_, so if record A matches Record B and Record B matches C, we put records A, B, and C in the same cluster. That is how different records in a cluster get matched at various confidence levels to the rest and all these records showed up together.
-* Our recommendation is to keep a threshold below **x**% of the max score as suspect records - manual review/to be visited later/flow through but will lessen confidence. **x** will depend on how accurate you find the results and how much you want to control the outcome.
+* Identify the records you consider **suspect** — those with a score below a threshold you set based on your own accuracy needs and how much you want to control the outcome. The exact threshold depends on your data and the quality of the results you observe, so inspect a sample of clusters and adjust accordingly.
 * Keep cluster size above 4 or 5 for inspection. You could keep it irrespective of the score, or look at only those clusters whose **z\_minScore** is 0. This will depend on what the results look like to you.
diff --git a/docs/security-and-privacy/security-and-privacy.md b/docs/security-and-privacy/security-and-privacy.md
index c9640594b..666cfcb09 100644
--- a/docs/security-and-privacy/security-and-privacy.md
+++ b/docs/security-and-privacy/security-and-privacy.md
@@ -20,7 +20,15 @@ Ensure your configured paths and tables sit within your security perimeter - pri
 
 #### Zingg Artefacts in Your Environment
 
-<table><thead><tr><th width="168.16796875" valign="top">Artefact</th><th width="145.21875" valign="top">Spark deployment location</th><th width="147.03125" valign="top">Snowflake Enterprise location</th><th valign="top">Contents</th></tr></thead><tbody><tr><td valign="top"><strong>Training data - unmarked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/unmarked/</code></td><td valign="top"><code>@stage/zingg_models/modelId/trainingData/unmarked/</code></td><td valign="top">Candidate record pairs selected by Zingg for labelling. Contains field values from your input dataset plus Zingg metadata columns.</td></tr><tr><td valign="top"><strong>Training data - marked</strong></td><td valign="top"><code>zinggDir/modelId/trainingData/marked/</code></td><td valign="top"><code>@stage/zingg_models/modelId/trainingData/marked/</code></td><td valign="top">Pairs after you have labelled them as Match, No Match, or Uncertain. Same field columns as unmarked plus the label column.</td></tr><tr><td valign="top"><strong>Trained model</strong></td><td valign="top"><code>zinggDir/modelId/model/</code></td><td valign="top"><code>@stage/zingg_models/modelId/model/</code></td><td valign="top">Snowpark ML model artefacts encoding the blocking and similarity learned from your training data. Does not contain raw input records.</td></tr><tr><td valign="top"><strong>Match output</strong></td><td valign="top">The <code>output</code> path configured in your <code>EArguments</code> / <code>Arguments</code></td><td valign="top">Snowflake table configured in <code>output</code> section</td><td valign="top">All input fields plus Zingg-generated columns (<code>Z_CLUSTER</code>, <code>Z_MINSCORE</code>, <code>Z_MAXSCORE</code> in Community; <code>ZINGG_ID</code> and Enterprise equivalents in Enterprise).</td></tr><tr><td valign="top"><strong>Stopwords</strong> (if configured)</td><td valign="top"><code>zinggDir/modelId/stopWords/&#x3C;columnName></code></td><td valign="top"><code>@stage/zingg_models/modelId/stopWords/&#x3C;columnName></code></td><td valign="top">List of high-frequency words detected for the specified column.</td></tr><tr><td valign="top"><strong>Output statistics</strong> (Enterprise)</td><td valign="top">The <code>outputStats</code> path configured</td><td valign="top"><code>@stage/zingg_models/modelId/outputStats/</code></td><td valign="top">Three statistics files per run: summary, cluster, and record-level metrics.</td></tr><tr><td valign="top"><strong>Credentials</strong></td><td valign="top">Not stored by Zingg</td><td valign="top">Managed by Snowflake</td><td valign="top">Connection credentials in <code>config.json</code> or environment variables remain under your control. Snowflake Enterprise uses Snowflake's native role and credential model.</td></tr></tbody></table>
+| Artefact | Spark deployment location | Snowflake Enterprise location | Contents |
+|---|---|---|---|
+| **Training data - unmarked** | `zinggDir/modelId/trainingData/unmarked/` | `@stage/zingg_models/modelId/trainingData/unmarked/` | Candidate record pairs selected by Zingg for labelling. Contains field values from your input dataset plus Zingg metadata columns. |
+| **Training data - marked** | `zinggDir/modelId/trainingData/marked/` | `@stage/zingg_models/modelId/trainingData/marked/` | Pairs after you have labelled them as Match, No Match, or Uncertain. Same field columns as unmarked plus the label column. |
+| **Trained model** | `zinggDir/modelId/model/` | `@stage/zingg_models/modelId/model/` | Snowpark ML model artefacts encoding the blocking and similarity learned from your training data. Does not contain raw input records. |
+| **Match output** | The `output` path configured in your `EArguments` / `Arguments` | Snowflake table configured in `output` section | All input fields plus Zingg-generated columns (`Z_CLUSTER`, `Z_MINSCORE`, `Z_MAXSCORE` in Community; `ZINGG_ID` and Enterprise equivalents in Enterprise). |
+| **Stopwords** (if configured) | `zinggDir/modelId/stopWords/<columnName>` | `@stage/zingg_models/modelId/stopWords/<columnName>` | List of high-frequency words detected for the specified column. |
+| **Output statistics** (Enterprise) | The `outputStats` path configured | `@stage/zingg_models/modelId/outputStats/` | Three statistics files per run: summary, cluster, and record-level metrics. |
+| **Credentials** | Not stored by Zingg | Managed by Snowflake | Connection credentials in `config.json` or environment variables remain under your control. Snowflake Enterprise uses Snowflake's native role and credential model. |
 
 ### Handling sensitive fields
 
diff --git a/docs/security-and-privacy/telemetry-and-usage-metrics.md b/docs/security-and-privacy/telemetry-and-usage-metrics.md
index d227f769b..ade28fcdb 100644
--- a/docs/security-and-privacy/telemetry-and-usage-metrics.md
+++ b/docs/security-and-privacy/telemetry-and-usage-metrics.md
@@ -16,7 +16,17 @@ Telemetry events are sent to Zingg servers when `collectMetrics` is `true` (the
 
 ### **What is captured**
 
-<table><thead><tr><th width="251.61328125" valign="top">Metric</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top">Data source type</td><td valign="top">Format of your data source (e.g. CSV, Snowflake, Parquet)</td></tr><tr><td valign="top">Fields count</td><td valign="top">Number of fields configured for matching</td></tr><tr><td valign="top">Record count</td><td valign="top">Total number of records passing through Zingg</td></tr><tr><td valign="top">Execution time</td><td valign="top">Duration of the phase run</td></tr><tr><td valign="top">Running phase</td><td valign="top">Which Zingg phase was executed</td></tr><tr><td valign="top">Matches and non-matches</td><td valign="top">Count of matched and non-matched records in the model</td></tr><tr><td valign="top">JDK information</td><td valign="top">JDK version and flavour</td></tr><tr><td valign="top">OS information</td><td valign="top">Operating system type</td></tr><tr><td valign="top">Zingg version</td><td valign="top">Version of Zingg being used</td></tr></tbody></table>
+| Metric | Description |
+|---|---|
+| Data source type | Format of your data source (e.g. CSV, Snowflake, Parquet) |
+| Fields count | Number of fields configured for matching |
+| Record count | Total number of records passing through Zingg |
+| Execution time | Duration of the phase run |
+| Running phase | Which Zingg phase was executed |
+| Matches and non-matches | Count of matched and non-matched records in the model |
+| JDK information | JDK version and flavour |
+| OS information | Operating system type |
+| Zingg version | Version of Zingg being used |
 
 No field values, record content, or personally identifiable information is included in any telemetry event.
 
diff --git a/docs/standardize-fields-and-results.md b/docs/standardize-fields-and-results.md
index 537175cf6..f75ccde4f 100644
--- a/docs/standardize-fields-and-results.md
+++ b/docs/standardize-fields-and-results.md
@@ -20,7 +20,13 @@ Use it when matched records contain the same entity but use different representa
 
 ### **What it does and does not do**
 
-<table><thead><tr><th width="248.80859375" valign="top">Behavior</th><th valign="top">Details</th></tr></thead><tbody><tr><td valign="top">When it runs</td><td valign="top">After matching, before writing the output.<br>It does not affect matching behaviour it transforms output values only</td></tr><tr><td valign="top">What it applies to</td><td valign="top">Any field you configure it on. One field can have one mapping file.</td></tr><tr><td valign="top">What it does not apply to</td><td valign="top">Primary key fields. These are never postprocessed.</td></tr><tr><td valign="top">Case sensitivity</td><td valign="top">Lookup is case-insensitive. CEO, ceo, and Ceo all match<br>the same mapping entry.</td></tr><tr><td valign="top">What it does not handle</td><td valign="top">Extra whitespace, punctuation differences, or partial matches. Those need to be cleaned upstream.</td></tr></tbody></table>
+| Behavior | Details |
+|---|---|
+| When it runs | After matching, before writing the output. It does not affect matching behaviour it transforms output values only |
+| What it applies to | Any field you configure it on. One field can have one mapping file. |
+| What it does not apply to | Primary key fields. These are never postprocessed. |
+| Case sensitivity | Lookup is case-insensitive. CEO, ceo, and Ceo all match the same mapping entry. |
+| What it does not handle | Extra whitespace, punctuation differences, or partial matches. Those need to be cleaned upstream. |
 
 ### Where this is useful
 
diff --git a/docs/stepbystep/configuration/adv-matchtypes.md b/docs/stepbystep/configuration/adv-matchtypes.md
index 5a81bdc02..7d453cb4f 100644
--- a/docs/stepbystep/configuration/adv-matchtypes.md
+++ b/docs/stepbystep/configuration/adv-matchtypes.md
@@ -8,9 +8,9 @@ description: Defining match types to match better and faster
 
 **Leveraging domain expertise to push matching accuracy. Also save time massaging data before matching.**
 
-While the Zingg matching algorithms learn the variations from the user labels, completely different attributes pose a challenge to matching accuracy. In the case of nicknames, or company abberviations, domain input can greatly enhance matching accuracy. Zingg's `MAPPING` match type is built for such cases. &#x20;
+While the Zingg matching algorithms learn the variations from the user labels, completely different attributes pose a challenge to matching accuracy. In the case of nicknames, or company abberviations, domain input can greatly enhance matching accuracy. Zingg's `MAPPING` match type is built for such cases.  
 
-To leverage user expertise for nicknames for example, the user can supply Zingg with a mapping json file `nicknames.json` which denotes nicknames. Zingg has prebuilt mappings for company names, nicknames etc. Or you can define your own mappings too.&#x20;
+To leverage user expertise for nicknames for example, the user can supply Zingg with a mapping json file `nicknames.json` which denotes nicknames. Zingg has prebuilt mappings for company names, nicknames etc. Or you can define your own mappings too. 
 
 Here is the structure of the json:
 
@@ -27,7 +27,7 @@ Here is the structure of the json:
 ]   
 </code></pre>
 
-Each line here represents common nicknames which represent the same name.&#x20;
+Each line here represents common nicknames which represent the same name. 
 
 To use this mapping within Zingg, define the field's match type as `MAPPING_<filename>` which in our case would be `mapping_nicknames`
 
@@ -43,7 +43,7 @@ To use this mapping within Zingg, define the field's match type as `MAPPING_<fil
 
 ### Transform and Match
 
-The `MAPPING` match type can also be used to transform and normalise categorical data. Let us say different data sources have different representations of gender. In one, gender is represented as M and F, in another it is noted as Male, Female and in the third as 1 and 2. Instead of transforming the gender column beforehand, one could create a mapping json called `gender.json` .&#x20;
+The `MAPPING` match type can also be used to transform and normalise categorical data. Let us say different data sources have different representations of gender. In one, gender is represented as M and F, in another it is noted as Male, Female and in the third as 1 and 2. Instead of transforming the gender column beforehand, one could create a mapping json called `gender.json` . 
 
 <pre><code><strong>[  
 </strong>  ["M", "Male", "1"],
@@ -51,7 +51,7 @@ The `MAPPING` match type can also be used to transform and normalise categorical
 ]  
 </code></pre>
 
-When we use it for the gender field like below, Zingg would automatically handle the transformation so that you don't have to.&#x20;
+When we use it for the gender field like below, Zingg would automatically handle the transformation so that you don't have to. 
 
 ```
 "fieldDefinition":[
diff --git a/docs/stepbystep/configuration/data-input-and-output/README.md b/docs/stepbystep/configuration/data-input-and-output/README.md
index 3d8bec032..f93d5b3c2 100644
--- a/docs/stepbystep/configuration/data-input-and-output/README.md
+++ b/docs/stepbystep/configuration/data-input-and-output/README.md
@@ -6,4 +6,4 @@ description: >-
 
 # Data Input and Output
 
-This section describes how to configure Zingg to connect to cloud warehouses, file formats, graphs, and database systems.&#x20;
+This section describes how to configure Zingg to connect to cloud warehouses, file formats, graphs, and database systems. 
diff --git a/docs/stepbystep/configuration/field-definitions.md b/docs/stepbystep/configuration/field-definitions.md
index 5bc438332..81643a5c0 100644
--- a/docs/stepbystep/configuration/field-definitions.md
+++ b/docs/stepbystep/configuration/field-definitions.md
@@ -18,7 +18,7 @@ The **name** of the field from the input data schema.
 
 **fields**
 
-To be defined later. For now, please keep this as the `fieldName`
+The name of the **input column(s)** that populate this field. When a single input column feeds the field, set this to that column name. To combine multiple source columns into one derived field, list them comma-separated — for example, an `address` field built from `address_line1,address_line2,city,state,postal_code`. The output column takes the `fieldName`. In most cases where the field maps one-to-one to a source column, set `fields` to the same value as `fieldName`.
 
 **dataType**
 
@@ -34,7 +34,7 @@ Type of the column - `string, integer, double`, etc.
 |                          FUZZY | Broad matches tolerant to typos, abbreviations, and other variations. Uses fuzzy string similarity features.                                                                                                                                                                                   | string, integer, long, double, date                |
 |                FUZZY_OPTIMISED | Same semantics as FUZZY but uses an optimized implementation. Provides similar matching quality with significantly lower CPU and memory usage on large datasets. Recommended for production/large-scale matching when FUZZY quality is desired but performance is critical. [Zingg Enterprise Feature](#user-content-fn-1)[^1] | string, integer, long, double, date                                             |
 |                          EXACT | Exact match with no tolerance for variation. Preferable for country codes, pin codes, and other categorical variables where you expect no variations.                                                                                                                                          | string, integer, long, date, boolean               |
-|                       DONT_USE | Included in the output but no computation is done on this field. Useful for IDs required in the output. DONT_USE fields are not shown to the labeler when [showConcise](label.md) is set to true.                                                                                              | any                                                |
+|                       DONT_USE | Included in the output but no computation is done on this field. Useful for IDs required in the output. DONT_USE fields are not shown to the labeler when `showConcise` is set to true.                                                                                              | any                                                |
 |                          EMAIL | Matches only the local part (before the @) of email addresses.                                                                                                                                                                                                                                 | string                                             |
 |                EMAIL_OPTIMISED | Same semantics as EMAIL but uses an optimized implementation for much faster evaluation on large datasets while preserving match behavior. Recommended for large-scale runs where many emails are compared. [Zingg Enterprise Feature](#user-content-fn-1)[^1]                                                                                   | string                                             |
 |                        PINCODE | Matches postal / pin codes (supports typical local formats such as `xxxxx` or `xxxxx-xxxx` depending on data).                                                                                                                                                                                 | string                                             |
diff --git a/docs/stepbystep/createtrainingdata/generatingdocumentation.md b/docs/stepbystep/createtrainingdata/generatingdocumentation.md
index db07ca97b..2b800fb1c 100644
--- a/docs/stepbystep/createtrainingdata/generatingdocumentation.md
+++ b/docs/stepbystep/createtrainingdata/generatingdocumentation.md
@@ -5,7 +5,7 @@ description: >-
 
 # Documenting The Training Data
 
-We may need to send our labeled data for a subject matter expert to review. We may also like to understand and analyse our training data. In such scenarios, we can document the records we have marked during the **label** phase by invoking the **generateDocs** phase.&#x20;
+We may need to send our labeled data for a subject matter expert to review. We may also like to understand and analyse our training data. In such scenarios, we can document the records we have marked during the **label** phase by invoking the **generateDocs** phase. 
 
 During **generateDocs**, Zingg generates readable documentation about the training data, including those marked as matches, as well as non-matches. The documentation is written to the **zinggDir/modelId** folder and can be built using the following
 
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md
index 66bd87b69..b61995c43 100644
--- a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/enterprise-snowflake-instructions.md
@@ -1,33 +1,67 @@
 ---
 description: >-
-  Complete guide for running Zingg Enterprise natively on Snowflake using Snowpark.
-  Covers installation, configuration, SQL/Python APIs, incremental matching, and
-  B2B customer/vendor entity resolution examples.
+  Complete guide for running Zingg Enterprise natively on Snowflake using Snowpark. Covers installation, configuration, SQL/Python APIs, incremental matching, and B2B customer/vendor entity resolution examples.
 parent: Installing Zingg Enterprise for Snowflake
 nav_order: 1
 ---
 
-# Zingg Enterprise Snowflake: Complete Instructions
+# Zingg Enterprise on Snowflake
 
-{% hint style="info" icon="right-long" %}
-**Enterprise only.** Zingg Enterprise runs natively inside Snowflake using Snowpark — no EC2, no Docker, no Lambda, no external Spark clusters. All compute executes on your Snowflake warehouse.
+{% hint style="success" icon="right-long" %}
+**Resolve customer and vendor master data natively inside Snowflake — no EC2, no Docker, no Lambda, no external Spark clusters.** All compute runs on your Snowflake warehouse, so your data never leaves.
 {% endhint %}
 
+{% embed url="https://www.youtube.com/watch?v=zOabyZxN9b0" %}
+Watch Zingg's entity resolution demo to see the end-to-end pipeline in action.
+{% endembed %}
+
+* **Zero infrastructure** — runs on your existing Snowflake warehouse
+* **Zero data egress** — customer/vendor data never leaves Snowflake
+* **Persistent `ZINGG_ID`** — globally unique entity IDs that survive incremental runs
+* **Incremental matching** — add new records without full reprocessing
+* **Snowsight labeling UI** — Match / Non-Match / Can't Say for data stewards
+
 ---
 
-## Why Snowflake Native?
+## What You'll Build
+
+By the end of this guide you'll have a working **B2B Entity Resolution pipeline** that resolves:
+
+* **B2B Customer Accounts** — parent companies + subsidiaries ("Alphabet Inc." + "Alphabet LLC" + "Google LLC" → one entity)
+* **Enterprise Vendor Profiles** — suppliers across SAP, Oracle, Coupa, Ariba into a unified golden record
+
+The full flow: **Find Training Data → Label → Train → Match → Incremental**.
+
+---
+
+## Architecture At A Glance
+
+```
+         ┌─────────────────────────────────────────────────────────┐
+         │                     SNOWFLAKE ACCOUNT                     │
+         │                                                           │
+         │   WAREHOUSE (compute)    DATABASE (tables)    STAGE       │
+         │   ZINGG_WH (auto-scale)  ZINGG_ENTITY_        @ZINGG_     │
+         │                          RESOLUTION           MODELS     │
+         │         │                     │                │          │
+         │         ▼                     ▼                ▼          │
+         │   ┌──────────────────── SNOWPARK ────────────────────┐    │
+         │   │  findTrainingData  →  label  →  train  →  match  │    │
+         │   └──────────────────────────────────────────────────┘    │
+         └─────────────────────────────────────────────────────────┘
+```
+
+{% hint style="info" %}
+**No infrastructure to manage.** Zingg Enterprise runs inside Snowpark Container Services, so you configure a warehouse size and go. There's nothing to patch, version, or scale yourself.
+{% endhint %}
 
-| Traditional (Community) | Zingg Enterprise Snowflake |
-|-------------------------|----------------------------|
-| Provision EC2 / Databricks / EMR | Zero infrastructure — runs on your Snowflake warehouse |
-| Manage Spark jars, versions, clusters | Snowpark handles runtime; you only configure warehouse size |
-| Data moves out of Snowflake for processing | **Zero data egress** — compute pushes to data |
-| Lambda + API Gateway for async phases | Native Snowflake procedures / Snowpark Container Services |
-| Separate monitoring (CloudWatch, Spark UI) | Unified in Snowflake Query History & Task Graphs |
+> **Product screenshot placeholder:** Zingg Enterprise pipeline overview (Snowsight). Add the screenshot here.
 
 ---
 
-## Prerequisites
+## 1. Prerequisites
+
+Before you begin, make sure you have:
 
 | Requirement | Details |
 |-------------|---------|
@@ -37,53 +71,41 @@ nav_order: 1
 | **Stage** | Internal named stage for model artifacts (e.g., `@ZINGG_MODELS`) |
 | **License** | Zingg Enterprise license file (`zingg.license`) |
 | **Python** | 3.8+ with `snowflake-snowpark-python` ≥ 1.12 |
-| **Network** | Snowflake account allows outbound to `pypi.org` (for pip install) or use internal PyPI mirror |
+
+{% hint style="warning" icon="warning" %}
+Some organizations can't reach `pypi.org` directly. If you're air-gapped or restricted, use your internal PyPI mirror during the manual install (see Step 3 below).
+{% endhint %}
 
 ---
 
-## Architecture Overview
+## 2. Install Zingg Enterprise
 
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                        YOUR SNOWFLAKE ACCOUNT                    │
-│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
-│  │  WAREHOUSE   │  │   DATABASE   │  │      NAMED STAGE     │  │
-│  │  (Compute)   │◄─┤  ZINGG_ER    │◄─┤  @ZINGG_MODELS       │  │
-│  │  AUTO-SCALE  │  │  (Tables)    │  │  (Model Artifacts)   │  │
-│  └──────┬───────┘  └──────┬───────┘  └──────────┬───────────┘  │
-│         │                 │                      │             │
-│         ▼                 ▼                      ▼             │
-│  ┌──────────────────────────────────────────────────────────┐  │
-│  │              SNOWPARK CONTAINER SERVICES                  │  │
-│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────────┐  │  │
-│  │  │  FIND   │  │ LABEL   │  │ TRAIN   │  │   MATCH     │  │  │
-│  │  │ TRAINING│  │  (UI)   │  │         │  │             │  │  │
-│  │  └────┬────┘  └────┬────┘  └────┬────┘  └──────┬──────┘  │  │
-│  └───────┼────────────┼────────────┼────────────┼───────────┘  │
-│          │            │            │            │              │
-└──────────┼────────────┼────────────┼────────────┼──────────────┘
-           │            │            │            │
-    ┌──────┴──────┐ ┌───┴────┐ ┌─────┴────┐ ┌────┴─────┐
-    │  Snowsight  │ │ Jupyter│ │  SQL/    │ │  Python  │
-    │  Labeling   │ │ Notebook│ │  Python  │ │  Worksheets│
-    └─────────────┘ └────────┘ └──────────┘ └───────────┘
-```
+Two ways to install — pick the one that fits your environment.
 
----
+{% stepper %}
+{% step %}
+### Choose an installation method
+
+* **Snowflake Marketplace (Recommended)** — ~5 minutes, fully automated setup of warehouse, stage, and roles. Best for most users.
+* **Manual Package Install** — ~15 minutes. For air-gapped, restricted, or highly customized environments.
+{% endstep %}
 
-## Installation Methods
+{% step %}
+### Method A: Snowflake Marketplace
 
-### Method 1: Snowflake Marketplace (Recommended — 5 minutes)
+1. Log into **Snowsight** → **Marketplace** → search **"Zingg Enterprise"**
+2. Click **Get** → select your target database/schema → **Install**
+3. Run the setup wizard (configures warehouse, stage, and roles automatically)
+4. Verify with: `CALL ZINGG.SYSTEM$VERIFY_SETUP();`
 
-1. Log into Snowsight → **Marketplace** → Search **"Zingg Enterprise"**
-2. Click **Get** → Select target database/schema → **Install**
-3. Run the setup wizard (configures warehouse, stage, roles automatically)
-4. Verify: `CALL ZINGG.SYSTEM$VERIFY_SETUP();`
+> **Product screenshot placeholder:** the Snowsight Marketplace listing for **Zingg Enterprise** with the Get / Install flow. Add the screenshot here.
+{% endstep %}
 
-### Method 2: Manual Package Install (For air-gapped / custom deployments)
+{% step %}
+### Method B: Manual Package Install
 
 ```bash
-# 1. Download Enterprise package (provided by Zingg after license purchase)
+# 1. Download the Enterprise package (provided by Zingg after license purchase)
 # Package name: zingg-enterprise-snowflake-<version>.tar.gz
 
 # 2. Extract
@@ -93,42 +115,25 @@ cd zingg-enterprise-snowflake-<version>
 
 # 3. Install Python dependencies
 pip install snowflake-snowpark-python==1.12.*
-pip install -r requirements.txt  # includes zingg-enterprise-snowflake wheel
+pip install -r requirements.txt   # includes the zingg-enterprise-snowflake wheel
 
-# 4. Place license file
+# 4. Place your license file
 cp /path/to/zingg.license .
 ```
+{% endstep %}
+{% endstepper %}
 
----
-
-## Configuration: B2B Customer & Vendor Entity Resolution
-
-The following examples use **B2B Customer Accounts** (parent companies + subsidiaries) and **Enterprise Vendor Profiles** (suppliers across ERPs) as the canonical entities.
-
-### 1. Snowpark Session Setup
-
-```python
-from snowflake.snowpark import Session
+{% hint style="success" icon="right-long" %}
+**Done with install?** Skip ahead to **Step 4 – Configure & Run** for the pipeline walkthrough, or follow the full configuration below.
+{% endhint %}
 
-# Use key-pair auth for production (see Security section below)
-connection_parameters = {
-    "account": "xy12345.us-east-1",           # Your Snowflake account identifier
-    "user": "ZINGG_SERVICE_USER",             # Dedicated service user
-    "private_key_file": "/path/to/rsa_key.p8", # Key-pair auth (recommended)
-    # "password": "********",                 # Or password auth for dev only
-    "role": "ZINGG_ER_ROLE",                  # Custom role with required grants
-    "warehouse": "ZINGG_WH",                  # Dedicated warehouse (auto-scale)
-    "database": "ZINGG_ENTITY_RESOLUTION",    # Target database
-    "schema": "PUBLIC"                        # Target schema
-}
+---
 
-session = Session.builder.configs(connection_parameters).create()
-session.sql("USE WAREHOUSE ZINGG_WH").collect()
-```
+## 3. Configure Snowflake Objects & Connection
 
-### 2. Create Required Snowflake Objects
+### 3.1 Create required Snowflake objects
 
-Run once per environment (DBA or ACCOUNTADMIN):
+Run this **once per environment** (as DBA or ACCOUNTADMIN):
 
 ```sql
 -- 1. Dedicated warehouse with auto-scaling
@@ -157,12 +162,39 @@ GRANT USAGE ON SCHEMA ZINGG_ENTITY_RESOLUTION.PUBLIC TO ROLE ZINGG_ER_ROLE;
 GRANT CREATE TABLE, CREATE VIEW, CREATE STAGE ON SCHEMA ZINGG_ENTITY_RESOLUTION.PUBLIC TO ROLE ZINGG_ER_ROLE;
 GRANT READ, WRITE ON STAGE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS TO ROLE ZINGG_ER_ROLE;
 
--- 5. Grant to service user
+-- 5. Grant to the service user
 GRANT ROLE ZINGG_ER_ROLE TO USER ZINGG_SERVICE_USER;
 ALTER USER ZINGG_SERVICE_USER SET DEFAULT_ROLE = ZINGG_ER_ROLE;
 ```
 
-### 3. Zingg Configuration (Python Dict)
+### 3.2 Create a Snowpark session
+
+```python
+from snowflake.snowpark import Session
+
+# Use key-pair auth for production (see Security below)
+connection_parameters = {
+    "account": "xy12345.us-east-1",            # Your Snowflake account identifier
+    "user": "ZINGG_SERVICE_USER",              # Dedicated service user
+    "private_key_file": "/path/to/rsa_key.p8", # Key-pair auth (recommended)
+    # "password": "********",                  # Or password auth for dev only
+    "role": "ZINGG_ER_ROLE",                   # Custom role with required grants
+    "warehouse": "ZINGG_WH",                   # Dedicated auto-scale warehouse
+    "database": "ZINGG_ENTITY_RESOLUTION",     # Target database
+    "schema": "PUBLIC"                         # Target schema
+}
+
+session = Session.builder.configs(connection_parameters).create()
+session.sql("USE WAREHOUSE ZINGG_WH").collect()
+```
+
+---
+
+## 4. Configure & Run the Pipeline
+
+The steps below use a **B2B Customer Accounts** example end-to-end. A **Vendor Profiles** example is included in the expandable section at the end.
+
+### Step 4.1 Define your field configuration
 
 ```python
 from zingg_enterprise import ZinggEnterprise
@@ -172,20 +204,20 @@ from zingg_enterprise import ZinggEnterprise
 customer_config = {
     "data": [{
         "name": "customer_input",
-        "table": "RAW_CUSTOMERS",           # Source table in ZINGG_ENTITY_RESOLUTION.PUBLIC
+        "table": "RAW_CUSTOMERS",            # Source table in ZINGG_ENTITY_RESOLUTION.PUBLIC
         "database": "ZINGG_ENTITY_RESOLUTION",
         "schema": "PUBLIC"
     }],
     "output": [{
         "name": "customer_resolved",
-        "table": "RESOLVED_CUSTOMERS",      # Output table (created by Zingg)
+        "table": "RESOLVED_CUSTOMERS",       # Output table (created by Zingg)
         "database": "ZINGG_ENTITY_RESOLUTION",
         "schema": "PUBLIC"
     }],
     "modelId": "b2b_customer_v1",
     "zinggDir": "@ZINGG_MODELS/b2b_customer_v1",  # Stage path for model artifacts
-    "numPartitions": 8,                     # Tune to warehouse: 2-4 × cluster count
-    "labelDataSampleSize": 0.3,             # 30% sample for training pair generation
+    "numPartitions": 8,                      # Tune: 2–4 × cluster count
+    "labelDataSampleSize": 0.3,              # 30% sample for training pair generation
     "fieldDefinition": [
         # Company name — fuzzy with MAPPING for known aliases
         {
@@ -194,28 +226,28 @@ customer_config = {
             "fields": "company_name",
             "dataType": "string"
         },
-        # Legal entity suffix — exact match on normalized suffix
+        # Legal entity suffix — exact on normalized suffix
         {
             "fieldName": "legal_suffix",
             "matchType": "EXACT",
             "fields": "legal_suffix",
             "dataType": "string"
         },
-        # Address — fuzzy optimized for street variations
+        # Address — fuzzy, optimized for street variations
         {
             "fieldName": "address",
             "matchType": "FUZZY_OPTIMISED",
             "fields": "address_line1,address_line2,city,state,postal_code,country",
             "dataType": "string"
         },
-        # Website domain — exact match (strong signal for B2B)
+        # Website domain — exact match (strong B2B signal)
         {
             "fieldName": "website_domain",
             "matchType": "EXACT",
             "fields": "website_domain",
             "dataType": "string"
         },
-        # Parent company ID — deterministic match when available
+        # Parent company ID — deterministic when available
         {
             "fieldName": "parent_company_id",
             "matchType": "EXACT",
@@ -237,17 +269,25 @@ customer_config = {
             "dataType": "string"
         }
     ],
-    # Optional: Deterministic rules for high-confidence auto-merge
+    # Deterministic rules for high-confidence auto-merge
     "deterministicMatching": [
-        {"fields": ["duns_number"]},                           # Same D-U-N-S = same vendor
-        {"fields": ["website_domain", "company_name"]},        # Same domain + name = same
-        {"fields": ["parent_company_id", "legal_suffix"]}      # Same parent + suffix
+        {"fields": ["duns_number"]},                    # Same D-U-N-S = same vendor
+        {"fields": ["website_domain", "company_name"]}, # Same domain + name = same
+        {"fields": ["parent_company_id", "legal_suffix"]}
     ],
-    # Optional: Pass-through for inactive records
+    # Pass-through for inactive records
     "passthroughExpr": "status = 'INACTIVE' AND status IS NOT NULL"
 }
 
-# ─── ENTERPRISE VENDOR PROFILES EXAMPLE ───
+zingg_customer = ZinggEnterprise(session, customer_config)
+```
+
+> **Tip:** When a field maps one-to-one to a source column, set `fields` to the same value as `fieldName`. When you want to combine multiple columns into a single derived field (like `address` above), list them comma-separated in `fields`.
+
+<details>
+<summary><strong>Enterprise Vendor Profiles example (SAP, Oracle, Coupa, Ariba)</strong></summary>
+
+```python
 # Resolves vendors across SAP, Oracle, Coupa, Ariba
 vendor_config = {
     "data": [{
@@ -279,19 +319,16 @@ vendor_config = {
         {"fields": ["email_domain", "vendor_name"]}
     ]
 }
-
-# Initialize Zingg Enterprise
-zingg_customer = ZinggEnterprise(session, customer_config)
 zingg_vendor = ZinggEnterprise(session, vendor_config)
 ```
+</details>
 
----
-
-## Mapping Files for B2B Aliases (MAPPING Match Type)
+<details>
+<summary><strong>Mapping files for B2B aliases (MAPPING match type)</strong></summary>
 
-Create JSON mapping files and upload to `@ZINGG_MODELS/mappings/`:
+Create JSON mapping files and upload them to `@ZINGG_MODELS/mappings/`. These map different name variants to a canonical value so they match even before similarity scoring runs.
 
-**`company_aliases.json`** — Parent/subsidiary relationships
+**`company_aliases.json`** — parent/subsidiary relationships:
 ```json
 [
   ["Alphabet Inc.", "Alphabet LLC", "Google LLC", "Google Inc.", "Google"],
@@ -302,7 +339,7 @@ Create JSON mapping files and upload to `@ZINGG_MODELS/mappings/`:
 ]
 ```
 
-**`vendor_aliases.json`** — Common vendor name variations
+**`vendor_aliases.json`** — common vendor name variations:
 ```json
 [
   ["Dell Technologies", "Dell Inc.", "Dell", "Dell EMC"],
@@ -319,71 +356,98 @@ PUT file:///local/path/company_aliases.json @ZINGG_MODELS/mappings/ AUTO_COMPRES
 PUT file:///local/path/vendor_aliases.json @ZINGG_MODELS/mappings/ AUTO_COMPRESS=FALSE;
 ```
 
-Reference in config: `"matchType": "MAPPING_company_aliases,FUZZY"` (filename without `.json`)
+Then reference the file by its base name (without `.json`) in your match type:
+`"matchType": "MAPPING_company_aliases,FUZZY"`
+</details>
 
----
+### Step 4.2 Run the phases
+
+{% stepper %}
+{% step %}
+### 1. Find Training Data
 
-## Running the Full Pipeline
+Generates candidate record pairs for labeling.
 
-### Phase 1: Find Training Data
 ```python
-# Generates candidate pairs for labeling
 zingg_customer.find_training_data()
-# Output: candidate pairs written to @ZINGG_MODELS/b2b_customer_v1/trainingData/unmarked/
+# → candidate pairs written to @ZINGG_MODELS/b2b_customer_v1/trainingData/unmarked/
 ```
+{% endstep %}
+
+{% step %}
+### 2. Label Pairs (Human Input)
 
-### Phase 2: Label Pairs (Interactive)
+The only step requiring a human. No ML knowledge needed — just your domain understanding.
 
-**Option A: Snowsight Labeling UI (Recommended for Data Stewards)**
+**Option A — Snowsight Labeling UI (recommended for data stewards):**
 ```python
-# Opens interactive labeling in Snowsight
-zingg_customer.label()
+zingg_customer.label()   # opens interactive labeling in Snowsight
 ```
-- Shows record pairs side-by-side with Match / Non-Match / Can't Say buttons
-- Auto-saves labels to `@ZINGG_MODELS/b2b_customer_v1/trainingData/marked/`
-- Target: 40-60 Match pairs + 40-60 Non-Match pairs before training
+* See record pairs side-by-side with **Match / Non-Match / Can't Say** buttons
+* Auto-saves labels to `@ZINGG_MODELS/b2b_customer_v1/trainingData/marked/`
+* **Target: 40–60 Match + 40–60 Non-Match pairs** before training
 
-**Option B: Jupyter / Python Worksheet (For ML Engineers)**
+> **Product screenshot placeholder:** the Snowsight labeling UI showing two candidate customer records with Match / Non-Match / Can't Say buttons. Add the screenshot here.
+
+**Option B — Jupyter / Python worksheet (for ML engineers):**
 ```python
-zingg_customer.label()  # Returns widget in Jupyter
-# Or use programmatic labeling for bulk review
+zingg_customer.label()   # returns an interactive widget in Jupyter
 ```
+{% endstep %}
+
+{% step %}
+### 3. Generate Documentation (Audit Before Train)
+
+Produces an HTML review report of all labeled pairs.
 
-### Phase 3: Generate Documentation (Audit Before Train)
 ```python
-# Produces HTML report of all labeled pairs
 zingg_customer.generate_docs()
-# Output: @ZINGG_MODELS/b2b_customer_v1/docs/model.html + data.html
+# → @ZINGG_MODELS/b2b_customer_v1/docs/model.html + data.html
 ```
+
 **Review checklist:**
 - [ ] False positives < 2% (different entities labeled as Match)
 - [ ] False negatives < 2% (same entities labeled as Non-Match)
 - [ ] All field types represented in training pairs
 - [ ] Edge cases covered: abbreviations, missing fields, transliterations
+{% endstep %}
+
+{% step %}
+### 4. Train the Model
+
+Builds the blocking + similarity models from your labels.
 
-### Phase 4: Train Model
 ```python
-# Builds blocking + similarity models from labels
 zingg_customer.train()
-# Model artifacts saved to @ZINGG_MODELS/b2b_customer_v1/model/
+# → model artifacts saved to @ZINGG_MODELS/b2b_customer_v1/model/
 ```
+{% endstep %}
+
+{% step %}
+### 5. Match (Resolve Entities)
+
+Runs full resolution on the input table.
 
-### Phase 5: Match (Resolve Entities)
 ```python
-# Full resolution on input table
 results = zingg_customer.match()
 results.show()
-
-# Output table RESOLVED_CUSTOMERS contains:
-# - All input columns
-# - ZINGG_ID: Persistent entity GUID (e.g., "ZNGG_7f3a2b1c...")
-# - Z_MINSCORE: Weakest link confidence in cluster
-# - Z_MAXSCORE: Strongest link confidence in cluster
 ```
 
-### Phase 6: Incremental Matching (Production)
+The `RESOLVED_CUSTOMERS` output table contains:
+* All your input columns
+* **`ZINGG_ID`** — persistent entity GUID (e.g., `ZNGG_7f3a2b1c...`)
+* **`Z_MINSCORE`** — weakest link confidence within the cluster
+* **`Z_MAXSCORE`** — strongest link confidence within the cluster
+
+> **Product screenshot placeholder:** the `RESOLVED_CUSTOMERS` match output in Snowsight showing the `ZINGG_ID`, `Z_MINSCORE`, and `Z_MAXSCORE` columns. Add the screenshot here.
+{% endstep %}
+
+{% step %}
+### 6. Incremental Matching (Production)
+
+Add new/modified records **without reprocessing everything**.
+
 ```python
-# For daily/weekly incremental loads
 incremental_config = {
     **customer_config,
     "incremental": True,
@@ -392,24 +456,30 @@ incremental_config = {
 
 zingg_incremental = ZinggEnterprise(session, incremental_config)
 results = zingg_incremental.match()
-# Merges new records into existing ZINGG_ID clusters
-# Preserves human-approved decisions from label phase
+# → merges new records into existing ZINGG_ID clusters
+# → preserves human-approved decisions from the label phase
 ```
+{% endstep %}
+{% endstepper %}
+
+{% hint style="info" %}
+**Prefer SQL?** Zingg Enterprise also exposes a SQL API for analysts and no-code workflows — see **Step 5 – SQL API** below.
+{% endhint %}
 
 ---
 
-## SQL API (For Analysts / No-Code Workflows)
+## 5. SQL API (For Analysts / No-Code Workflows)
 
-After Marketplace install or manual stored procedure deployment:
+After the Marketplace install or manual stored-procedure deployment, run the whole pipeline from SQL:
 
 ```sql
 -- 1. Find training data
 CALL ZINGG.FIND_TRAINING_DATA('b2b_customer_v1');
 
--- 2. Label interactively in Snowsight (opens labeling UI)
+-- 2. Label interactively in Snowsight (opens the labeling UI)
 CALL ZINGG.LABEL('b2b_customer_v1');
 
--- 3. Train model
+-- 3. Train the model
 CALL ZINGG.TRAIN('b2b_customer_v1');
 
 -- 4. Run match
@@ -424,17 +494,20 @@ SELECT * FROM TABLE(ZINGG.GET_STATISTICS('b2b_customer_v1'));
 
 ---
 
-## Interpreting Results: B2B Score Bands
+## 6. Interpreting Results: B2B Score Bands
+
+Use these score bands to decide what happens with each resolved cluster:
 
 | Score Range | Tier | Action | B2B Example |
 |-------------|------|--------|-------------|
-| **0.90 – 1.00** | 🎯 **Auto-Merge** | Flow directly to golden record | Same D-U-N-S + exact company name |
-| **0.75 – 0.89** | ⚠️ **Steward Review** | Route to Data Steward queue | "Google LLC" vs "Alphabet Inc." (alias mapping) |
-| **0.50 – 0.74** | 🔍 **Deep Review** | Senior steward + SME consult | Partial address match, missing fields |
-| **Below 0.50** | 🛑 **Separate Entities** | Keep as distinct records | Different tax IDs, different domains |
+| **0.90 – 1.00** | **Auto-Merge** | Flow directly to golden record | Same D-U-N-S + exact company name |
+| **0.75 – 0.89** | **Steward Review** | Route to Data Steward queue | "Google LLC" vs "Alphabet Inc." (alias mapping) |
+| **0.50 – 0.74** | **Deep Review** | Senior steward + SME consult | Partial address match, missing fields |
+| **Below 0.50** | **Separate Entities** | Keep as distinct records | Different tax IDs, different domains |
+
+Route results automatically using a view:
 
 ```sql
--- Auto-route to review queues
 CREATE OR REPLACE VIEW RESOLVED_CUSTOMERS_REVIEW AS
 SELECT *,
   CASE
@@ -448,12 +521,14 @@ FROM ZINGG_ENTITY_RESOLUTION.PUBLIC.RESOLVED_CUSTOMERS;
 
 ---
 
-## Monitoring & Operations
+## 7. Monitoring & Operations
+
+<details>
+<summary><strong>Query History — built-in observability</strong></summary>
 
-### Query History (Built-in Observability)
 ```sql
 -- All Zingg job runs
-SELECT QUERY_ID, QUERY_TEXT, EXECUTION_STATUS, 
+SELECT QUERY_ID, QUERY_TEXT, EXECUTION_STATUS,
        TOTAL_ELAPSED_TIME/1000 AS seconds,
        BYTES_SCANNED, BYTES_WRITTEN
 FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
@@ -467,8 +542,11 @@ FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
 WHERE QUERY_TEXT ILIKE '%ZINGG%'
 GROUP BY WAREHOUSE_NAME;
 ```
+</details>
+
+<details>
+<summary><strong>Model health metrics</strong></summary>
 
-### Model Health Metrics
 ```python
 # Get comprehensive statistics
 stats = zingg_customer.get_statistics()
@@ -481,25 +559,29 @@ print(f"Max cluster size: {stats['summary']['max_cluster_size']}")
 cluster_stats = stats['cluster']
 for cluster in cluster_stats:
     if cluster['z_cluster_determinism'] < 0.3:
-        print(f"Cluster {cluster['z_cluster']}: Low determinism ({cluster['z_cluster_determinism']:.2f}) — review recommended")
+        print(f"Cluster {cluster['z_cluster']}: Low determinism "
+              f"({cluster['z_cluster_determinism']:.2f}) — review recommended")
 ```
+</details>
 
 ---
 
-## Security Best Practices
+## 8. Security Best Practices
+
+<details>
+<summary><strong>1. Key-pair authentication (production required)</strong></summary>
 
-### 1. Key-Pair Authentication (Production Required)
 ```bash
-# Generate RSA key pair
+# Generate an RSA key pair
 openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt
 openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub
 
-# Assign public key to service user
+# Assign the public key to the service user
 ALTER USER ZINGG_SERVICE_USER SET RSA_PUBLIC_KEY='MIIBIjANBgkqh...';
 ```
 
 ```python
-# Use in connection parameters
+# Use it in connection parameters
 connection_parameters = {
     "account": "...",
     "user": "ZINGG_SERVICE_USER",
@@ -508,71 +590,81 @@ connection_parameters = {
     ...
 }
 ```
+</details>
+
+<details>
+<summary><strong>2. Network policy</strong></summary>
 
-### 2. Network Policy
 ```sql
--- Restrict Zingg service user to corporate IPs
+-- Restrict the Zingg service user to corporate IPs
 CREATE NETWORK POLICY ZINGG_NETWORK_POLICY
-  ALLOWED_IP_LIST = ('203.0.113.0/24', '198.51.100.0/24');  # Your office/CI/CD ranges
+  ALLOWED_IP_LIST = ('203.0.113.0/24', '198.51.100.0/24');  -- Your office/CI ranges
 ALTER USER ZINGG_SERVICE_USER SET NETWORK_POLICY = ZINGG_NETWORK_POLICY;
 ```
+</details>
+
+<details>
+<summary><strong>3. Column-level security (PII)</strong></summary>
 
-### 3. Column-Level Security (PII)
 ```sql
--- Mask SSN / Tax ID in output for non-privileged roles
+-- Mask SSN / Tax ID for non-privileged roles
 CREATE MASKING POLICY MASK_TAX_ID AS (val STRING) RETURNS STRING ->
   CASE WHEN CURRENT_ROLE() IN ('ZINGG_ER_ROLE', 'ACCOUNTADMIN') THEN val ELSE '*****' END;
 
 ALTER TABLE RESOLVED_VENDORS MODIFY COLUMN TAX_ID SET MASKING POLICY MASK_TAX_ID;
 ```
+</details>
+
+<details>
+<summary><strong>4. Audit logging</strong></summary>
 
-### 4. Audit Logging
 ```sql
--- Enable query audit for Zingg role
+-- Enable query audit for the Zingg role
 ALTER ACCOUNT SET LOG_LEVEL = 'FULL';  -- Requires ACCOUNTADMIN
 -- All Zingg queries now logged in SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
 ```
+</details>
 
 ---
 
-## Troubleshooting Quick Reference
+## 9. Troubleshooting Quick Reference
 
 | Symptom | Cause | Fix |
 |---------|-------|-----|
 | `Session creation failed: Invalid private key` | Key format / path wrong | Ensure PKCS#8 DER format, correct path, no passphrase |
-| `Stage @ZINGG_MODELS does not exist` | Setup wizard skipped | Run `CREATE STAGE` SQL from Configuration section |
-| `Out of memory` on train phase | Warehouse too small | Increase warehouse size or `numPartitions` |
-| `Labeling UI won't load` | Snowsight permissions | Grant `SNOWSIGHT_USER` role to service user |
-| `Incremental run duplicates ZINGG_ID` | `lastRunTimestamp` incorrect | Use `CURRENT_TIMESTAMP() - INTERVAL '1 DAY'` pattern |
-| `MAPPING match type not found` | Mapping file not on stage | `PUT` mapping JSON to `@ZINGG_MODELS/mappings/` |
+| `Stage @ZINGG_MODELS does not exist` | Setup wizard skipped | Run the `CREATE STAGE` SQL from the configuration section |
+| `Out of memory` on train phase | Warehouse too small | Increase warehouse size or tune `numPartitions` |
+| `Labeling UI won't load` | Snowsight permissions | Grant `SNOWSIGHT_USER` role to the service user |
+| `Incremental run duplicates ZINGG_ID` | `lastRunTimestamp` wrong | Use a `CURRENT_TIMESTAMP() - INTERVAL '1 DAY'` pattern |
+| `MAPPING match type not found` | Mapping file not on stage | `PUT` the mapping JSON to `@ZINGG_MODELS/mappings/` |
 | `Deterministic rule not firing` | Field type mismatch | Ensure `EXACT` match type on deterministic fields |
 
 ---
 
-## Performance Tuning Guide
+## 10. Performance Tuning
 
 | Parameter | Starting Value | Tuning Rule |
 |-----------|----------------|-------------|
-| `numPartitions` | 8 | 2–4 × warehouse cluster count (MAX_CLUSTER_COUNT) |
+| `numPartitions` | 8 | 2–4 × warehouse cluster count (`MAX_CLUSTER_COUNT`) |
 | `labelDataSampleSize` | 0.3 | Reduce to 0.1 for >1M records; increase for <100K |
-| Warehouse size | MEDIUM | Scale up for train/match; down for label |
+| Warehouse size | MEDIUM | Scale up for train/match; scale down for label |
 | `MAX_CLUSTER_COUNT` | 4 | Increase for parallel phase execution |
 
 ---
 
-## Version Upgrade Procedure
+## 11. Version Upgrade Procedure
 
 ```bash
-# 1. Backup current model stage
+# 1. Back up your current model stage
 CREATE STAGE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS_BACKUP
   CLONE ZINGG_ENTITY_RESOLUTION.PUBLIC.ZINGG_MODELS;
 
-# 2. Download new Enterprise package
-# 3. Install new Python wheel
+# 2. Download the new Enterprise package
+# 3. Install the new Python wheel
 pip install --upgrade zingg-enterprise-snowflake-<new_version>.whl
 
 # 4. Re-run train (models are version-compatible forward)
-zingg_customer.train()  # Rebuilds with new version
+zingg_customer.train()   # Rebuilds with the new version
 
 # 5. Verify output
 results = zingg_customer.match()
@@ -589,19 +681,19 @@ ALTER STAGE ZINGG_MODELS_BACKUP RENAME TO ZINGG_MODELS;
 
 | Topic | Link |
 |-------|------|
-| Snowflake Connector (Community) | [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md) |
-| Platform Guide: Snowflake | [Platform Guide for Snowflake](../platform-guides/platform-guide-for-snowflake.md) |
-| Python API Reference | [Enterprise ZinggES Python API](../zingg-python-api/enterprise-zingges-python-api.md) |
-| Match Types Reference | [Field Definition Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/) |
-| Deterministic Matching | [Set Deterministic Matching Conditions](../running-zingg/set-deterministic-matching-conditions.md) |
-| Pass-Through Records | [Pass Through](../running-zingg/pass-through.md) |
-| Incremental Matching | [Run Incremental Matching](../running-zingg/run-incremental-matching.md) |
+| Snowflake Connector (Community) | [Connect Snowflake](../../../connect-your-data/connect-cloud-warehouses/connect-snowflake.md) |
+| Platform Guide: Snowflake | [Platform Guide for Snowflake](../../../platform-guides/platform-guide-for-snowflake.md) |
+| Python API Reference | [Enterprise ZinggES Python API](../../../zingg-python-api/enterprise-zingges-python-api.md) |
+| Match Types Reference | [Field Definition Match Types](../../../zingg-concepts/zingg-configuration/field-definition/match-types/) |
+| Deterministic Matching | [Set Deterministic Matching Conditions](../../../running-zingg/set-deterministic-matching-conditions.md) |
+| Pass-Through Records | [Pass Through](../../../running-zingg/pass-through.md) |
+| Incremental Matching | [Run Incremental Matching](../../../running-zingg/run-incremental-matching.md) |
 
 ---
 
 ## Support & Licensing
 
-- **Enterprise License Required**: Contact [Zingg Sales](https://www.zingg.ai/company/contact/contact)
-- **Documentation Issues**: GitHub Issues on `zinggAI/zingg` with label `docs:snowflake`
-- **Technical Support**: Included with Enterprise license — dedicated Slack channel
-- **Training**: Zingg offers on-site Data Steward labeling workshops for B2B/Vendor ER
\ No newline at end of file
+* **Enterprise License Required** — contact [Zingg Sales](https://www.zingg.ai/company/contact/contact)
+* **Technical Support** — included with your Enterprise license (dedicated Slack channel)
+* **Documentation Issues** — GitHub Issues on `zinggAI/zingg` with label `docs:snowflake`
+* **Training** — Zingg offers on-site Data Steward labeling workshops for B2B/Vendor ER
diff --git a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
index df16087dc..bff61b85f 100644
--- a/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
+++ b/docs/stepbystep/installation/installing-zingg-enterprise-snowflake/snowflake-properties.md
@@ -111,6 +111,6 @@ ALTER USER zingg_user SET RSA_PUBLIC_KEY='MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCg
 
 ### Related Documentation
 
-* [Platform Guide for Snowflake](../../platform-guides/platform-guide-for-snowflake.md)
-* [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md)
-* [Zingg Runtime Properties](../stepbystep/zingg-runtime-properties.md)
\ No newline at end of file
+* [Platform Guide for Snowflake](../../../platform-guides/platform-guide-for-snowflake.md)
+* [Connect Snowflake](../../../connect-your-data/connect-cloud-warehouses/connect-snowflake.md)
+* [Zingg Runtime Properties](../../../reference/runtime-properties.md)
\ No newline at end of file
diff --git a/docs/tuning/blocking-strategy.md b/docs/tuning/blocking-strategy.md
index 1d98ce38f..aef8a4737 100644
--- a/docs/tuning/blocking-strategy.md
+++ b/docs/tuning/blocking-strategy.md
@@ -76,5 +76,5 @@ args.setBlockingModel("WIDER")
 * Set up field definitions and the arguments object this key belongs to → [Configure Zingg](../running-zingg/configure-zingg.md)
 * Check block-size distribution and coverage after changing strategy → [Verify Blocking](../running-zingg/create-training-data/verify-blocking.md)
 * Blocking model concept and how it fits in the pipeline → [Blocking Model](../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
-* Define your own blocking functions for specialized data patterns → [Custom Blocking and Similarity](/broken/pages/DWdyf7az3MmhJaVca3k7)
+* Define your own blocking functions for specialized data patterns → [Blocking Model](../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
 {% endhint %}
diff --git a/docs/tuning/configure-field-standardization.md b/docs/tuning/configure-field-standardization.md
index 58791d2bd..b52d54c20 100644
--- a/docs/tuning/configure-field-standardization.md
+++ b/docs/tuning/configure-field-standardization.md
@@ -46,7 +46,11 @@ Create a JSON file where each element is an array of equivalent values. The firs
 
 #### **Three rules your mapping file must follow**
 
-<table><thead><tr><th width="236.6796875">Rule</th><th>Why it matters</th></tr></thead><tbody><tr><td>First value is canonical</td><td>All variants in the array map to the first value in the output. <code>["Chief Executive Officer", "CEO"]</code> → output will always say "Chief Executive Officer". Order your arrays with the canonical form first.</td></tr><tr><td>Lookup is case-insensitive</td><td>"CEO", "ceo", and "Ceo" all match the same entry. You do not need separate entries for casing variants.</td></tr><tr><td>Rows must be disjoint</td><td>A string must not appear in two different arrays. If "CEO" appears in two separate canonical groups, Zingg's behaviour is undefined. Each value belongs to exactly one canonical group.</td></tr></tbody></table>
+| Rule | Why it matters |
+|---|---|
+| First value is canonical | All variants in the array map to the first value in the output. `["Chief Executive Officer", "CEO"]` → output will always say "Chief Executive Officer". Order your arrays with the canonical form first. |
+| Lookup is case-insensitive | "CEO", "ceo", and "Ceo" all match the same entry. You do not need separate entries for casing variants. |
+| Rows must be disjoint | A string must not appear in two different arrays. If "CEO" appears in two separate canonical groups, Zingg's behaviour is undefined. Each value belongs to exactly one canonical group. |
 
 {% hint style="info" icon="right-long" %}
 Place your mapping JSON file in the directory from which you are executing Zingg. Reference it by base filename (without the `.json` extension) in your configuration. For example, a file named `jobtitles.json` is referenced as `jobtitles`.
@@ -112,7 +116,9 @@ Use `STANDARDISE_<basename>` to reference a mapping file named `<basename>.json`
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-**CONTENT FOR THIS SECTION TO BE PROVIDED BY SONAL LATER**
+{% hint style="info" %}
+**Coming soon** — Enterprise Snowflake guidance for field standardisation is in progress and will be published here when available.
+{% endhint %}
 {% endtab %}
 {% endtabs %}
 
diff --git a/docs/tuning/improve-accuracy/README.md b/docs/tuning/improve-accuracy/README.md
index 2a365925e..193c86a91 100644
--- a/docs/tuning/improve-accuracy/README.md
+++ b/docs/tuning/improve-accuracy/README.md
@@ -56,4 +56,4 @@ If Zingg is missing matches that you know exist (false negatives), the blocking
 ### Step 5: Custom blocking and similarity
 
 For advanced use cases, you can define your own blocking and similarity functions to replace\
-or extend Zingg's built-in approaches. See [Custom Blocking and Similarity](/broken/pages/DWdyf7az3MmhJaVca3k7) for advanced tuning approaches.
+or extend Zingg's built-in approaches. See the [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md) and [Similarity Model](../../zingg-concepts/how-zingg-learns/zingg-models/similarity-model.md) for advanced tuning approaches.
diff --git a/docs/zingg-concepts/how-zingg-learns/README.md b/docs/zingg-concepts/how-zingg-learns/README.md
index 753d574c0..5db2fd285 100644
--- a/docs/zingg-concepts/how-zingg-learns/README.md
+++ b/docs/zingg-concepts/how-zingg-learns/README.md
@@ -25,7 +25,18 @@ Zingg models understand your data, and are not a generic algorithm applied to it
 
 When you define your field configuration, you choose a match type for each field. Here is a starting point for the most common field types.
 
-<table><thead><tr><th width="265.6796875">Field type</th><th>Recommended match type + note</th></tr></thead><tbody><tr><td>First name, last name,<br>company name</td><td><code>FUZZY</code> - handles spelling variations<br>and abbreviations. Use <code>MAPPING</code> (Enterprise) for known<br>alias or nickname lists.</td></tr><tr><td>Email address</td><td><code>EMAIL</code> - matches the portion before the <code>@</code> only, avoiding mismatches from different domains for the same person.</td></tr><tr><td>Date of birth, registration date</td><td><code>EXACT</code> - dates should not have fuzzy tolerance.</td></tr><tr><td>Postal code, ZIP code</td><td><code>PINCODE</code> - handles common format variants.</td></tr><tr><td>Street address (full line)</td><td><code>FUZZY</code> - or <code>ONLY_ALPHABETS_FUZZY</code> combined with <code>NUMERIC</code> for the street number as a separate field.</td></tr><tr><td>Street number, apartment number</td><td><code>NUMERIC</code> - extracts and compares the number portion only.</td></tr><tr><td>National ID, SSN, tax ID</td><td><code>EXACT</code> - trusted identifiers should never have fuzzy tolerance. Also consider deterministic matching<br>(Enterprise) for these fields.</td></tr><tr><td>Internal record ID (not used for matching)</td><td><code>DONT_USE</code> - appears in output but excluded from comparison.</td></tr><tr><td>Fields frequently null across source systems</td><td>Add <code>NULL_OR_BLANK</code> alongside the main match type.</td></tr><tr><td>Product descriptions, free-text notes</td><td><code>TEXT</code> - word overlap comparison for longer free-text fields.</td></tr></tbody></table>
+| Field type | Recommended match type + note |
+|---|---|
+| First name, last name, company name | `FUZZY` - handles spelling variations and abbreviations. Use `MAPPING` (Enterprise) for known alias or nickname lists. |
+| Email address | `EMAIL` - matches the portion before the `@` only, avoiding mismatches from different domains for the same person. |
+| Date of birth, registration date | `EXACT` - dates should not have fuzzy tolerance. |
+| Postal code, ZIP code | `PINCODE` - handles common format variants. |
+| Street address (full line) | `FUZZY` - or `ONLY_ALPHABETS_FUZZY` combined with `NUMERIC` for the street number as a separate field. |
+| Street number, apartment number | `NUMERIC` - extracts and compares the number portion only. |
+| National ID, SSN, tax ID | `EXACT` - trusted identifiers should never have fuzzy tolerance. Also consider deterministic matching (Enterprise) for these fields. |
+| Internal record ID (not used for matching) | `DONT_USE` - appears in output but excluded from comparison. |
+| Fields frequently null across source systems | Add `NULL_OR_BLANK` alongside the main match type. |
+| Product descriptions, free-text notes | `TEXT` - word overlap comparison for longer free-text fields. |
 
 {% hint style="success" icon="right-long" %}
 **Read more:**
diff --git a/docs/zingg-concepts/how-zingg-learns/zingg-models/README.md b/docs/zingg-concepts/how-zingg-learns/zingg-models/README.md
index 2540e8b4c..68acc2a96 100644
--- a/docs/zingg-concepts/how-zingg-learns/zingg-models/README.md
+++ b/docs/zingg-concepts/how-zingg-learns/zingg-models/README.md
@@ -12,7 +12,7 @@ When you run the train phase, Zingg builds two separate machine learning models
 Understanding what each model does gives you a clear framework for diagnosing problems, tuning performance, and knowing which part of the pipeline to adjust when results are not what\
 you expect.
 
-Models are saved within the customer environment at a location of their choice.&#x20;
+Models are saved within the customer environment at a location of their choice. 
 
 
 
diff --git a/docs/zingg-concepts/z-cluster-and-zingg-id.md b/docs/zingg-concepts/z-cluster-and-zingg-id.md
index 92837ae18..be96efa19 100644
--- a/docs/zingg-concepts/z-cluster-and-zingg-id.md
+++ b/docs/zingg-concepts/z-cluster-and-zingg-id.md
@@ -45,7 +45,14 @@ However, if cluster membership changes significantly between runs. For example,
 
 #### **Lifecycle behaviour**
 
-<table><thead><tr><th width="306.1484375">Scenario</th><th>Zingg_ID behaviour</th></tr></thead><tbody><tr><td>New record matches an existing cluster</td><td>Inherits the cluster's existing <code>Zingg_ID</code></td></tr><tr><td>New record does not match any existing cluster</td><td>Receives a new <code>Zingg_ID</code></td></tr><tr><td>Cluster merges with another in an incremental run</td><td>Merge is handled automatically</td></tr><tr><td>Cluster splits due to new data or updated training</td><td><code>Zingg_ID</code> may be reassigned</td></tr><tr><td>Human-approved cluster decisions from previous runs</td><td>Preserved, not overridden</td></tr><tr><td>Model is retrained</td><td>Use <a href="../running-zingg/reassign-zingg-id.md">Reassign <code>Zingg_ID</code> </a>to carry existing IDs to the new model</td></tr></tbody></table>
+| Scenario | Zingg_ID behaviour |
+|---|---|
+| New record matches an existing cluster | Inherits the cluster's existing `Zingg_ID` |
+| New record does not match any existing cluster | Receives a new `Zingg_ID` |
+| Cluster merges with another in an incremental run | Merge is handled automatically |
+| Cluster splits due to new data or updated training | `Zingg_ID` may be reassigned |
+| Human-approved cluster decisions from previous runs | Preserved, not overridden |
+| Model is retrained | Use [Reassign `Zingg_ID` ](../running-zingg/reassign-zingg-id.md)to carry existing IDs to the new model |
 
 ### The `Zingg_ID` column in your output
 
diff --git a/docs/zingg-concepts/zingg-configuration/README.md b/docs/zingg-concepts/zingg-configuration/README.md
index a8ecb187e..ca8f40ee8 100644
--- a/docs/zingg-concepts/zingg-configuration/README.md
+++ b/docs/zingg-concepts/zingg-configuration/README.md
@@ -1,15 +1,15 @@
 # Zingg Configuration
 
-To resolve entities, Zingg needs some user inputs or arguments. These arguments help Zingg understand where the input data is, which fields to use in the matching and how, where the output is to be written, where the models are to be persisted or used from. The user can also provide some performance specific settings.&#x20;
+To resolve entities, Zingg needs some user inputs or arguments. These arguments help Zingg understand where the input data is, which fields to use in the matching and how, where the output is to be written, where the models are to be persisted or used from. The user can also provide some performance specific settings. 
 
-The configuration can be done either through JSON, or through Zingg's Python API.&#x20;
+The configuration can be done either through JSON, or through Zingg's Python API. 
 
 Here is some important terminology to be aware of:
 
-1. Arguments - representative of the user input comprising of input and output data, fields and their matching criteria, models location as well as performance criteria. Zingg Enterprise also has specialised arguments for functionality like incremental run, reassign and diff etc.&#x20;
-2. Pipes - Zingg's abstraction for the data store. A pipe encapsulates the source or destination of records; a delta file path, a Snowflake table, a UC table, an RDBMS dataset. Different pipes are configured by passing the format string in the configuration.&#x20;
+1. Arguments - representative of the user input comprising of input and output data, fields and their matching criteria, models location as well as performance criteria. Zingg Enterprise also has specialised arguments for functionality like incremental run, reassign and diff etc. 
+2. Pipes - Zingg's abstraction for the data store. A pipe encapsulates the source or destination of records; a delta file path, a Snowflake table, a UC table, an RDBMS dataset. Different pipes are configured by passing the format string in the configuration. 
 3. Field Definition - Field Definition is the configuration object that tells Zingg which fields to use for matching and how to compare them. Each field definition has four attributes: `fieldName` (the column name), `fields` (same as `fieldName` for now), `dataType` (string, integer, double, etc.), and `matchType` (the similarity functions to apply).
-4. Match type -&#x20;
+4. Match type - 
 
 {% hint style="success" icon="right-long" %}
 **Read more**: [Pipes and Data Connections](../../connect-your-data/pipes-and-data-connections.md)
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
index 13963ac06..fd51e496b 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
@@ -4,14 +4,29 @@ description: Abstraction for similarity algorithms in human understandable terms
 
 # Match Type
 
-Every field in your Zingg configuration gets a match type assigned to it. The match type tells\
-Zingg which similarity function to apply when comparing values in that field across two records. Each `dataType` has predefined features for the given `matchType`. For example, the `string` type with `FUZZY` match is configured for Affine and Jaro-Winkler string comparison.
+Every field in your Zingg configuration gets a match type assigned to it. The match type tells Zingg which similarity function to apply when comparing values in that field across two records. Each `dataType` has predefined features for the given `matchType`. For example, the `string` type with `FUZZY` match is configured for Affine and Jaro-Winkler string comparison.
 
 Choosing the right match type for each field is one of the highest-impact configuration decisions you make as it directly affects matching accuracy.
 
 Multiple match types can be assigned to a single field, separated by commas.
 
-<table><thead><tr><th width="139.8359375" valign="top">Match Type</th><th width="172.765625" valign="top">What it does</th><th valign="top">Data types</th><th valign="top">Best for</th><th width="102.7313232421875" valign="top">Edition</th><th width="187.5281982421875">Example</th></tr></thead><tbody><tr><td valign="top"><a href="fuzzy-match.md"><code>FUZZY</code></a></td><td valign="top">Broad matching tolerant to typos,<br>abbreviations and variations. Uses <code>fuzzy</code> string similarity features.</td><td valign="top">string, integer, long, double, date</td><td valign="top">Names, addresses, free-text fields</td><td valign="top">All</td><td>"Jon Smith", "Jonathan Smith", and<br>"J. Smith" all match the same record.</td></tr><tr><td valign="top"><a href="fuzzy_optimised-match.md"><code>FUZZY_OPTIMISED</code></a></td><td valign="top">Same accuracy as <code>FUZZY</code>, approximately 2X faster. Lower CPU and memory usage on large datasets. Use this in production when <code>FUZZY</code> accuracy is needed at scale</td><td valign="top">string, integer, long, double, date</td><td valign="top">Same as <code>FUZZY</code> - use when dataset<br>is large and performance matters</td><td valign="top">Enterprise only</td><td>Same matching behaviour as <code>FUZZY</code>. Processes a 10M-record dataset in approximately half the time.</td></tr><tr><td valign="top"><a href="exact-match.md"><code>EXACT</code></a></td><td valign="top">Provides an exact-match signal to the classifier. A strong indicator when two values align perfectly. Records may still match based on other fields and how training pairs were labeled.</td><td valign="top">string, integer, long, date, boolean</td><td valign="top">Country codes, boolean flags,<br>categorical fields where no<br>variation is expected</td><td valign="top">All</td><td>"US" and "US"—strong match signal. "US" and ""us"—no exact signal, but the record pair may still match if other fields score highly.</td></tr><tr><td valign="top"><a href="email-match.md"><code>EMAIL</code></a></td><td valign="top">Matches only the local part before<br>the <code>@</code> - ignores the domain entirely.</td><td valign="top">string</td><td valign="top">Email address fields</td><td valign="top">All</td><td>"john.smith@gmail.com" and<br>"john.smith@company.com" — match.<br>"john.smith@gmail.com" and<br>"jsmith@gmail.com" — no match.</td></tr><tr><td valign="top"><a href="email_optimised-match.md"><code>EMAIL_OPTIMISED</code></a></td><td valign="top">Same as <code>EMAIL</code> but significantly faster<br>evaluation on large datasets.</td><td valign="top">string</td><td valign="top">Email fields at production scale</td><td valign="top">Enterprise only</td><td>Same behavior as <code>EMAIL</code>.<br>Use when comparing millions of<br>email addresses.</td></tr><tr><td valign="top"><a href="pincode-match.md"><code>PINCODE</code></a></td><td valign="top">Matches postal and pin codes across<br>common format variants (e.g. xxxxx<br>and xxxxx-xxxx).</td><td valign="top">string</td><td valign="top">Postal/ZIP code fields</td><td valign="top">All</td><td>"94102" and "94102-1234" — match.<br>"94102" and "94103" — no match.</td></tr><tr><td valign="top"><a href="numeric-match.md"><code>NUMERIC</code></a></td><td valign="top">Extracts numbers from strings and<br>compares how many are the same<br>across both strings.</td><td valign="top">string</td><td valign="top">Street numbers, apartment numbers,<br>building numbers</td><td valign="top">All</td><td>"42B Main St" and "42 Main Street" —<br>the number 42 is extracted and matched<br>from both. Alphabetic variation ignored.</td></tr><tr><td valign="top"><a href="numeric_with_units-match.md"><code>NUMERIC_WITH_UNITS</code></a></td><td valign="top">Extracts product codes or numbers<br>with units (e.g. 16gb, 500ml) and<br>compares how many are the same.</td><td valign="top">string</td><td valign="top">Product specification fields,<br>size field</td><td valign="top">All</td><td>16GB" and "16 GB DDR4" — the 16 and "GB" tokens match. "16gb" and "32gb" — the units match but the numbers do not.</td></tr><tr><td valign="top"><a href="text-match.md"><code>TEXT</code></a></td><td valign="top">Compares overlapping words between two strings. Good for longer descriptive text without many typos.</td><td valign="top">string</td><td valign="top">Product descriptions, notes,<br>long free-text fields</td><td valign="top">All</td><td>"Enterprise data management platform"<br>and "data management platform for<br>enterprise" — high overlap, likely match.<br>"Enterprise software" and<br>"consumer hardware" — low overlap,<br>likely no match.</td></tr><tr><td valign="top"><a href="only_alphabets_exact-match.md"><code>ONLY_ALPHABETS_EXACT</code></a></td><td valign="top">Ignores all numbers, then requires an exact letter match.</td><td valign="top">string</td><td valign="top">Building names where unit numbers<br>should be ignored</td><td valign="top">All</td><td>"Tower A, Flat 12" and "Tower A, Flat 7"<br>— "Tower A" matches exactly after<br>numbers are stripped.<br>"Tower A" and "Tower B" — no match.</td></tr><tr><td valign="top"><a href="only_alphabets_fuzzy-match.md"><code>ONLY_ALPHABETS_FUZZY</code></a></td><td valign="top">Ignores all numbers, then applies fuzzy matching to alphabetic characters only</td><td valign="top">string</td><td valign="top">Address fields where street names<br>need fuzzy matching and numbers<br>are handled separately via <code>NUMERIC</code></td><td valign="top">All</td><td>"St. James Rd" and "Saint James Road" —<br>alphabetic characters compared fuzzily.<br>Combine with <code>NUMERIC</code> for the<br>street number.</td></tr><tr><td valign="top"><a href="only_alphabets_fuzzy_optimised-match.md"><code>ONLY_ALPHABETS_FUZZY_OPTIMISED</code></a></td><td valign="top">Same as <code>ONLY_ALPHABETS_FUZZY</code> but optimised for production scale.</td><td valign="top">string</td><td valign="top">Same as <code>ONLY_ALPHABETS_FUZZY</code> -use when dataset is large</td><td valign="top">Enterprise only</td><td>Same matching behaviour.<br>Use for large address datasets.</td></tr><tr><td valign="top"><a href="null_or_blank-match.md"><code>NULL_OR_BLANK</code></a></td><td valign="top">By default Zingg treats nulls as<br>matches. Add this alongside another<br>match type to build an explicit feature for null/blank values so the model can learn their effect.</td><td valign="top">string, integer, long, date, boolean</td><td valign="top">Any field frequently null or blank<br>across source systems -combine<br>with <code>FUZZY</code> or <code>EXACT</code></td><td valign="top">All</td><td>Field A: null, Field B: "John" — with <code>NULL_OR_BLANK</code> added, these are NOT treated as matching. Without it, null matches anything.</td></tr><tr><td valign="top"><a href="dont_use-match.md"><code>DONT_USE</code></a></td><td valign="top">Appears in output but no computation<br>is done. Not shown in the labeller<br>when <code>showConcise</code> is <code>true</code>.</td><td valign="top">any</td><td valign="top">Record IDs, internal keys,<br>any field needed in output<br>but not in matching</td><td valign="top">All</td><td>A <code>customer_id</code> field that must appear<br>in output for traceability but should<br>not influence whether two records<br>are the same entity.</td></tr><tr><td valign="top"><a href="mapping_-filename-match.md"><code>MAPPING_(FILENAME)</code></a></td><td valign="top">Maps input field values to canonical<br>values using a user-supplied lookup file. Handles nicknames, abbreviations,<br>company name variants, and categorical<br>normalisation. Matching is tolerant<br>to common variations defined in the mapping file.</td><td valign="top">string</td><td valign="top">Name fields (nicknames), company<br>fields, categorical fields with<br>different representations across<br>systems</td><td valign="top">Enterprise only</td><td>"Jon", "Jonathan", "Johnny" all map<br>to canonical "John" via nicknames.json.<br>"M", "Male", "1" all map to "M"<br>via gender.json. Matched before<br>similarity scoring runs.</td></tr></tbody></table>
+| Match Type | What it does | Data types | Best for | Edition | Example |
+|------------|--------------|------------|----------|---------|---------|
+| [`FUZZY`](fuzzy-match.md) | Broad matching tolerant to typos, abbreviations and variations. Uses `fuzzy` string similarity features. | string, integer, long, double, date | Names, addresses, free-text fields | All | "Jon Smith", "Jonathan Smith", and "J. Smith" all match the same record. |
+| [`FUZZY_OPTIMISED`](fuzzy_optimised-match.md) | Same accuracy as `FUZZY`, approximately 2X faster. Lower CPU and memory usage on large datasets. Use this in production when `FUZZY` accuracy is needed at scale. | string, integer, long, double, date | Same as `FUZZY` — use when dataset is large and performance matters | Enterprise only | Same matching behaviour as `FUZZY`. Processes a 10M-record dataset in approximately half the time. |
+| [`EXACT`](exact-match.md) | Provides an exact-match signal to the classifier. A strong indicator when two values align perfectly. Records may still match based on other fields and how training pairs were labeled. | string, integer, long, date, boolean | Country codes, boolean flags, categorical fields where no variation is expected | All | "US" and "US" — strong match signal. "US" and "us" — no exact signal, but the record pair may still match if other fields score highly. |
+| [`EMAIL`](email-match.md) | Matches only the local part before the `@` — ignores the domain entirely. | string | Email address fields | All | "john.smith@gmail.com" and "john.smith@company.com" — match. "john.smith@gmail.com" and "jsmith@gmail.com" — no match. |
+| [`EMAIL_OPTIMISED`](email_optimised-match.md) | Same as `EMAIL` but significantly faster evaluation on large datasets. | string | Email fields at production scale | Enterprise only | Same behavior as `EMAIL`. Use when comparing millions of email addresses. |
+| [`PINCODE`](pincode-match.md) | Matches postal and pin codes across common format variants (e.g. `xxxxx` and `xxxxx-xxxx`). | string | Postal/ZIP code fields | All | "94102" and "94102-1234" — match. "94102" and "94103" — no match. |
+| [`NUMERIC`](numeric-match.md) | Extracts numbers from strings and compares how many are the same across both strings. | string | Street numbers, apartment numbers, building numbers | All | "42B Main St" and "42 Main Street" — the number 42 is extracted and matched from both. Alphabetic variation ignored. |
+| [`NUMERIC_WITH_UNITS`](numeric_with_units-match.md) | Extracts product codes or numbers with units (e.g. 16gb, 500ml) and compares how many are the same. | string | Product specification fields, size fields | All | "16GB" and "16 GB DDR4" — the 16 and "GB" tokens match. "16gb" and "32gb" — the units match but the numbers do not. |
+| [`TEXT`](text-match.md) | Compares overlapping words between two strings. Good for longer descriptive text without many typos. | string | Product descriptions, notes, long free-text fields | All | "Enterprise data management platform" and "data management platform for enterprise" — high overlap, likely match. "Enterprise software" and "consumer hardware" — low overlap, likely no match. |
+| [`ONLY_ALPHABETS_EXACT`](only_alphabets_exact-match.md) | Ignores all numbers, then requires an exact letter match. | string | Building names where unit numbers should be ignored | All | "Tower A, Flat 12" and "Tower A, Flat 7" — "Tower A" matches exactly after numbers are stripped. "Tower A" and "Tower B" — no match. |
+| [`ONLY_ALPHABETS_FUZZY`](only_alphabets_fuzzy-match.md) | Ignores all numbers, then applies fuzzy matching to alphabetic characters only. | string | Address fields where street names need fuzzy matching and numbers are handled separately via `NUMERIC` | All | "St. James Rd" and "Saint James Road" — alphabetic characters compared fuzzily. Combine with `NUMERIC` for the street number. |
+| [`ONLY_ALPHABETS_FUZZY_OPTIMISED`](only_alphabets_fuzzy_optimised-match.md) | Same as `ONLY_ALPHABETS_FUZZY` but optimised for production scale. | string | Same as `ONLY_ALPHABETS_FUZZY` — use when dataset is large | Enterprise only | Same matching behaviour. Use for large address datasets. |
+| [`NULL_OR_BLANK`](null_or_blank-match.md) | By default Zingg treats nulls as matches. Add this alongside another match type to build an explicit feature for null/blank values so the model can learn their effect. | string, integer, long, date, boolean | Any field frequently null or blank across source systems — combine with `FUZZY` or `EXACT` | All | Field A: null, Field B: "John" — with `NULL_OR_BLANK` added, these are NOT treated as matching. Without it, null matches anything. |
+| [`DONT_USE`](dont_use-match.md) | Appears in output but no computation is done. Not shown in the labeller when `showConcise` is `true`. | any | Record IDs, internal keys, any field needed in output but not in matching | All | A `customer_id` field that must appear in output for traceability but should not influence whether two records are the same entity. |
+| [`MAPPING_(FILENAME)`](mapping_-filename-match.md) | Maps input field values to canonical values using a user-supplied lookup file. Handles nicknames, abbreviations, company name variants, and categorical normalisation. Matching is tolerant to common variations defined in the mapping file. | string | Name fields (nicknames), company fields, categorical fields with different representations across systems | Enterprise only | "Jon", "Jonathan", "Johnny" all map to canonical "John" via nicknames.json. "M", "Male", "1" all map to "M" via gender.json. Matched before similarity scoring runs. |
 
 {% hint style="success" icon="right-long" %}
 **Read More:**
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
index 687fe8d37..5b9a1cc9c 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
@@ -15,7 +15,12 @@ This handles the common scenario where the same person has a work email and a pe
 
 ### What `EMAIL` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">j.smith@company.com</td><td valign="top">j.smith@other.com</td><td valign="top">Yes - identical local part, domain ignored</td></tr><tr><td valign="top">j.smith@company.com</td><td valign="top">john.smith@company.com</td><td valign="top">Partial - local parts differ but share "smith"; scores a graded similarity, not a hard match or non-match</td></tr><tr><td valign="top">support@ibm.com</td><td valign="top">support@microsoft.com</td><td valign="top">Yes - identical local part, even though the organisations are unrelated</td></tr><tr><td valign="top">[null]</td><td valign="top">j.smith@company.com</td><td valign="top">Yes - a null/blank value on either side is an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| j.smith@company.com | j.smith@other.com | Yes - identical local part, domain ignored |
+| j.smith@company.com | john.smith@company.com | Partial - local parts differ but share "smith"; scores a graded similarity, not a hard match or non-match |
+| support@ibm.com | support@microsoft.com | Yes - identical local part, even though the organisations are unrelated |
+| [null] | j.smith@company.com | Yes - a null/blank value on either side is an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use `EMAIL`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
index 4e715a44b..01016cb82 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
@@ -18,7 +18,12 @@ The scoring pattern is exactly the same as `EMAIL` - only the computation time d
 
 ### What `EMAIL_OPTIMISED` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">j.smith@company.com</td><td valign="top">j.smith@other.com</td><td valign="top">Yes - same as <code>EMAIL</code>, identical local part, domain ignored</td></tr><tr><td valign="top">j.smith@company.com</td><td valign="top">john.smith@company.com</td><td valign="top">Partial - same as <code>EMAIL</code>, local parts differ but share "smith"; scores a graded similarity, not a hard match or non-match</td></tr><tr><td valign="top">support@ibm.com</td><td valign="top">support@microsoft.com</td><td valign="top">Yes - identical local part, even though the organisations are unrelated</td></tr><tr><td valign="top">[null]</td><td valign="top">j.smith@company.com</td><td valign="top">Yes - same as <code>EMAIL</code>, a null/blank value on either side is an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| j.smith@company.com | j.smith@other.com | Yes - same as `EMAIL`, identical local part, domain ignored |
+| j.smith@company.com | john.smith@company.com | Partial - same as `EMAIL`, local parts differ but share "smith"; scores a graded similarity, not a hard match or non-match |
+| support@ibm.com | support@microsoft.com | Yes - identical local part, even though the organisations are unrelated |
+| [null] | j.smith@company.com | Yes - same as `EMAIL`, a null/blank value on either side is an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use `EMAIL_OPTIMISED`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
index edd4a4db2..262dab08f 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
@@ -15,7 +15,17 @@ Use `EXACT` for any field where a difference in value means a definite differenc
 
 ### What `EXACT` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">123-45-6789</td><td valign="top">123-45-6789</td><td valign="top">Yes - identical</td></tr><tr><td valign="top">123-45-6789</td><td valign="top">123-45-6780</td><td valign="top">No - single-digit difference</td></tr><tr><td valign="top">US</td><td valign="top">US</td><td valign="top">Yes - identical</td></tr><tr><td valign="top">US</td><td valign="top">us</td><td valign="top">Yes - both values are lowercased by a case-normalization preprocessing step before the comparison runs, so "US" and "us" both become "us" and match</td></tr><tr><td valign="top">true</td><td valign="top">true</td><td valign="top">Yes</td></tr><tr><td valign="top">true</td><td valign="top">1</td><td valign="top">No - EXACT does plain equality with no boolean coercion, so "true" and "1" do not match</td></tr><tr><td valign="top">2024-01-15</td><td valign="top">2024-01-15</td><td valign="top">Yes</td></tr><tr><td valign="top">2024-01-15</td><td valign="top">01/15/2024</td><td valign="top">No - no date parsing under EXACT; differing formats are compared as literal strings</td></tr><tr><td valign="top">[null]</td><td valign="top">123-45-6789</td><td valign="top">Yes - EXACT treats a null on either side as an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| 123-45-6789 | 123-45-6789 | Yes - identical |
+| 123-45-6789 | 123-45-6780 | No - single-digit difference |
+| US | US | Yes - identical |
+| US | us | Yes - both values are lowercased by a case-normalization preprocessing step before the comparison runs, so "US" and "us" both become "us" and match |
+| true | true | Yes |
+| true | 1 | No - EXACT does plain equality with no boolean coercion, so "true" and "1" do not match |
+| 2024-01-15 | 2024-01-15 | Yes |
+| 2024-01-15 | 01/15/2024 | No - no date parsing under EXACT; differing formats are compared as literal strings |
+| [null] | 123-45-6789 | Yes - EXACT treats a null on either side as an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use `EXACT`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
index f6747a733..0748b8ee7 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
@@ -15,7 +15,14 @@ It is Zingg's most permissive match type and the right starting point for any fi
 
 ### What `FUZZY` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">Jonathan Smith</td><td valign="top">Jon Smith</td><td valign="top">Yes - abbreviation and short-form variation</td></tr><tr><td valign="top">J. Smith</td><td valign="top">John Smith</td><td valign="top">Yes - initial vs full name</td></tr><tr><td valign="top">Jonathon</td><td valign="top">Jonathan</td><td valign="top">Yes - common transposition</td></tr><tr><td valign="top">Johnson</td><td valign="top">Smith</td><td valign="top">No - too different, no common characters</td></tr><tr><td valign="top">IBM Corp</td><td valign="top">IBM Corporation</td><td valign="top">Yes - long shared prefix scores high</td></tr><tr><td valign="top">[null]</td><td valign="top">John Smith</td><td valign="top">Depends - add <code>NULL_OR_BLANK</code> to control null behaviour</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| Jonathan Smith | Jon Smith | Yes - abbreviation and short-form variation |
+| J. Smith | John Smith | Yes - initial vs full name |
+| Jonathon | Jonathan | Yes - common transposition |
+| Johnson | Smith | No - too different, no common characters |
+| IBM Corp | IBM Corporation | Yes - long shared prefix scores high |
+| [null] | John Smith | Depends - add `NULL_OR_BLANK` to control null behaviour |
 
 ### When to use `FUZZY`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
index f2133fb1a..2d0212df9 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
@@ -31,7 +31,14 @@ The mapping file lists groups of equivalent values. `MAPPING` is typically combi
 
 ### What `MAPPING_(FileName)` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Result with MAPPING + EXACT</th><th valign="top">Result with MAPPING + FUZZY</th></tr></thead><tbody><tr><td valign="top">Jon</td><td valign="top">Jonathan</td><td valign="top">Match - both normalize to the same representative value</td><td valign="top">Match</td></tr><tr><td valign="top">Bob</td><td valign="top">Robert</td><td valign="top">Match - both normalize to the same representative value</td><td valign="top">Match</td></tr><tr><td valign="top">Jon</td><td valign="top">Jhon</td><td valign="top">No match - "Jhon" isn't in the mapping, so it's compared as-is against "Jonathan"</td><td valign="top">Match - caught by FUZZY's tolerance for the misspelling</td></tr><tr><td valign="top">Jon</td><td valign="top">John</td><td valign="top">No match - neither "John" nor the mapped "Jonathan" are equal strings</td><td valign="top">Match - FUZZY's similarity scoring treats them as close enough, even though they're not listed as synonyms</td></tr><tr><td valign="top">Robert</td><td valign="top">William</td><td valign="top">No match - normalize to different representative values</td><td valign="top">No match - FUZZY also finds them too dissimilar</td></tr><tr><td valign="top">Jon</td><td valign="top">[null]</td><td valign="top">No match - nothing to compare against</td><td valign="top">No match - combine with <code>NULL_OR_BLANK</code> to handle explicitly</td></tr></tbody></table>
+| Value A | Value B | Result with MAPPING + EXACT | Result with MAPPING + FUZZY |
+|---|---|---|---|
+| Jon | Jonathan | Match - both normalize to the same representative value | Match |
+| Bob | Robert | Match - both normalize to the same representative value | Match |
+| Jon | Jhon | No match - "Jhon" isn't in the mapping, so it's compared as-is against "Jonathan" | Match - caught by FUZZY's tolerance for the misspelling |
+| Jon | John | No match - neither "John" nor the mapped "Jonathan" are equal strings | Match - FUZZY's similarity scoring treats them as close enough, even though they're not listed as synonyms |
+| Robert | William | No match - normalize to different representative values | No match - FUZZY also finds them too dissimilar |
+| Jon | [null] | No match - nothing to compare against | No match - combine with `NULL_OR_BLANK` to handle explicitly |
 
 **Example mapping file**
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
index 0217d541d..2a882d254 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
@@ -16,7 +16,13 @@ Use `NULL_OR_BLANK` combined with another match type, in the `matchType` string
 
 ### What **`NULL_OR_BLANK`** matches and what it does not
 
-<table data-header-hidden><thead><tr><th valign="top"></th><th valign="top"></th><th valign="top"></th><th valign="top"></th></tr></thead><tbody><tr><td valign="top">Value A</td><td valign="top">Value B</td><td valign="top"><code>NULL_OR_BLANK</code> feature</td><td valign="top">Notes</td></tr><tr><td valign="top">null</td><td valign="top">John Smith</td><td valign="top">0.0</td><td valign="top">One side null</td></tr><tr><td valign="top">[empty string]</td><td valign="top">John Smith</td><td valign="top">0.0</td><td valign="top">Empty string treated exactly like null</td></tr><tr><td valign="top">null</td><td valign="top">null</td><td valign="top">0.0</td><td valign="top">No distinction between one-sided and both-sided nulls</td></tr><tr><td valign="top">null</td><td valign="top">[empty string]</td><td valign="top">0.0</td><td valign="top">Null and blank are interchangeable</td></tr><tr><td valign="top">John Smith</td><td valign="top">John Smith</td><td valign="top">1.0</td><td valign="top">Both values present</td></tr></tbody></table>
+| Value A | Value B | `NULL_OR_BLANK` feature | Notes |
+|---|---|---|---|
+| null | John Smith | 0.0 | One side null |
+| [empty string] | John Smith | 0.0 | Empty string treated exactly like null |
+| null | null | 0.0 | No distinction between one-sided and both-sided nulls |
+| null | [empty string] | 0.0 | Null and blank are interchangeable |
+| John Smith | John Smith | 1.0 | Both values present |
 
 There is no distinction between "both null" and "one null, one populated" - all four null/blank rows above score identically.
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
index 6a4b7f403..a52167110 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
@@ -19,7 +19,15 @@ This makes it useful for address fields where a number is embedded in a string -
 
 ### What `NUMERIC` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Score</th><th valign="top">Notes</th></tr></thead><tbody><tr><td valign="top">42 Main St</td><td valign="top">42 Main Street</td><td valign="top">1.0 (Yes)</td><td valign="top">42 extracted from both</td></tr><tr><td valign="top">42B Main St</td><td valign="top">42 Main St</td><td valign="top">1.0 (Yes)</td><td valign="top">42 extracted from both - "B" isn't a digit, so it's dropped</td></tr><tr><td valign="top">42 Main St</td><td valign="top">43 Main St</td><td valign="top">0.0 (No)</td><td valign="top">{42} vs {43} share nothing</td></tr><tr><td valign="top">Suite 12, Floor 3</td><td valign="top">Suite 12, Floor 4</td><td valign="top">0.33</td><td valign="top">{12,3} vs {12,4} share one of three distinct numbers</td></tr><tr><td valign="top">10 Main St, Apt 10</td><td valign="top">10 Main St</td><td valign="top">1.0 (Yes)</td><td valign="top">"10" appears twice on the left but dedupes to the set {10}, same as the right side</td></tr><tr><td valign="top">[no numbers]</td><td valign="top">42 Main St</td><td valign="top">0.0 (No)</td><td valign="top">One side extracts no numbers, so there's nothing to intersect</td></tr><tr><td valign="top">[null]</td><td valign="top">42 Main St</td><td valign="top">0.0 (No)</td><td valign="top">The null-matches-anything default every other match type uses does not apply here</td></tr></tbody></table>
+| Value A | Value B | Score | Notes |
+|---|---|---|---|
+| 42 Main St | 42 Main Street | 1.0 (Yes) | 42 extracted from both |
+| 42B Main St | 42 Main St | 1.0 (Yes) | 42 extracted from both - "B" isn't a digit, so it's dropped |
+| 42 Main St | 43 Main St | 0.0 (No) | {42} vs {43} share nothing |
+| Suite 12, Floor 3 | Suite 12, Floor 4 | 0.33 | {12,3} vs {12,4} share one of three distinct numbers |
+| 10 Main St, Apt 10 | 10 Main St | 1.0 (Yes) | "10" appears twice on the left but dedupes to the set {10}, same as the right side |
+| [no numbers] | 42 Main St | 0.0 (No) | One side extracts no numbers, so there's nothing to intersect |
+| [null] | 42 Main St | 0.0 (No) | The null-matches-anything default every other match type uses does not apply here |
 
 ### When to use `NUMERIC`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
index 484624964..f9d689888 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
@@ -16,7 +16,15 @@ description: >-
 
 ### What `NUMERIC_WITH_UNITS` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Score</th><th valign="top">Notes</th></tr></thead><tbody><tr><td valign="top">16gb RAM</td><td valign="top">16 GB Memory</td><td valign="top">0.0 (No)</td><td valign="top"><code>{16gb}</code> vs <code>{16}</code> - the space in "16 GB" stops the unit from gluing to the number, so they extract as different tokens</td></tr><tr><td valign="top">16gb RAM</td><td valign="top">32gb RAM</td><td valign="top">0.0 (No)</td><td valign="top"><code>{16gb}</code> vs <code>{32gb}</code> share nothing</td></tr><tr><td valign="top">500ml</td><td valign="top">0.5L</td><td valign="top">0.0 (No)</td><td valign="top"><code>{500ml}</code> vs <code>{0.5L}</code> share nothing; there is no unit conversion</td></tr><tr><td valign="top">2.4GHz Dual Core</td><td valign="top">2.4GHz Processor</td><td valign="top">1.0 (Yes)</td><td valign="top"><code>2.4GHz</code> extracted from both - same glued format on both sides</td></tr><tr><td valign="top">16gb</td><td valign="top">16</td><td valign="top">0.0 (No)</td><td valign="top"><code>{16gb}</code> vs <code>{16}</code> share nothing</td></tr><tr><td valign="top">[null]</td><td valign="top">[null]</td><td valign="top">1.0 (Yes)</td><td valign="top">Both sides extract zero tokens - the auto-match case unique to this match type</td></tr><tr><td valign="top">[null]</td><td valign="top">16gb RAM</td><td valign="top">0.0 (No)</td><td valign="top">Only one side extracts zero tokens - falls back to a non-match, same as <code>NUMERIC</code></td></tr></tbody></table>
+| Value A | Value B | Score | Notes |
+|---|---|---|---|
+| 16gb RAM | 16 GB Memory | 0.0 (No) | `{16gb}` vs `{16}` - the space in "16 GB" stops the unit from gluing to the number, so they extract as different tokens |
+| 16gb RAM | 32gb RAM | 0.0 (No) | `{16gb}` vs `{32gb}` share nothing |
+| 500ml | 0.5L | 0.0 (No) | `{500ml}` vs `{0.5L}` share nothing; there is no unit conversion |
+| 2.4GHz Dual Core | 2.4GHz Processor | 1.0 (Yes) | `2.4GHz` extracted from both - same glued format on both sides |
+| 16gb | 16 | 0.0 (No) | `{16gb}` vs `{16}` share nothing |
+| [null] | [null] | 1.0 (Yes) | Both sides extract zero tokens - the auto-match case unique to this match type |
+| [null] | 16gb RAM | 0.0 (No) | Only one side extracts zero tokens - falls back to a non-match, same as `NUMERIC` |
 
 ### When to use `NUMERIC_WITH_UNITS`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
index ff8c2a5ef..d3d85fbe8 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
@@ -15,7 +15,14 @@ This is designed for situations where the numeric component of a value varies ac
 
 ### What **`ONLY_ALPHABETS_EXACT`** matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">Tower A, Flat 12</td><td valign="top">Tower A, Flat 7</td><td valign="top">Yes - both strip to "Tower A Flat", exact match</td></tr><tr><td valign="top">Tower A</td><td valign="top">Tower B</td><td valign="top">No - "Tower A" vs "Tower B" after stripping, different</td></tr><tr><td valign="top">Block 3, Wing East</td><td valign="top">Block 7, Wing East</td><td valign="top">Yes - both strip to "Block Wing East",<br>exact match</td></tr><tr><td valign="top">Block 3, Wing East</td><td valign="top">Block 3, Wing West</td><td valign="top">No - "Wing East" vs "Wing West"</td></tr><tr><td valign="top">42nd Floor, Suite A</td><td valign="top">18th Floor, Suite A</td><td valign="top">No - strips to "nd Floor, Suite A" vs "th Floor, Suite A"; the ordinal suffix letters survive stripping and differ</td></tr><tr><td valign="top">[null]</td><td valign="top">Tower A, Flat 12</td><td valign="top">Yes - a null/blank value on either side is an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| Tower A, Flat 12 | Tower A, Flat 7 | Yes - both strip to "Tower A Flat", exact match |
+| Tower A | Tower B | No - "Tower A" vs "Tower B" after stripping, different |
+| Block 3, Wing East | Block 7, Wing East | Yes - both strip to "Block Wing East", exact match |
+| Block 3, Wing East | Block 3, Wing West | No - "Wing East" vs "Wing West" |
+| 42nd Floor, Suite A | 18th Floor, Suite A | No - strips to "nd Floor, Suite A" vs "th Floor, Suite A"; the ordinal suffix letters survive stripping and differ |
+| [null] | Tower A, Flat 12 | Yes - a null/blank value on either side is an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use **`ONLY_ALPHABETS_EXACT`**
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
index a101d6786..0709c3170 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
@@ -13,7 +13,12 @@ description: >-
 
 ### What **`ONLY_ALPHABETS_FUZZY`** matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">42 Main Street</td><td valign="top">44 Main Street</td><td valign="top">Yes - numbers stripped, "Main Street" is identical</td></tr><tr><td valign="top">42 Main St</td><td valign="top">44 Main Street</td><td valign="top">Yes - "Main St" vs "Main Street" scores a high fuzzy similarity</td></tr><tr><td valign="top">42 Main St</td><td valign="top">42 Oak St</td><td valign="top">No - "Main St" vs "Oak St" too different alphabetically</td></tr><tr><td valign="top">[null]</td><td valign="top">Main Street</td><td valign="top">Yes - a null/blank value on either side is an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| 42 Main Street | 44 Main Street | Yes - numbers stripped, "Main Street" is identical |
+| 42 Main St | 44 Main Street | Yes - "Main St" vs "Main Street" scores a high fuzzy similarity |
+| 42 Main St | 42 Oak St | No - "Main St" vs "Oak St" too different alphabetically |
+| [null] | Main Street | Yes - a null/blank value on either side is an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use **`ONLY_ALPHABETS_FUZZY`**
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
index 509a3d908..1008e7fac 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
@@ -18,7 +18,12 @@ The scoring pattern is exactly the same as `ONLY_ALPHABETS_FUZZY` - only the com
 
 ### What **`ONLY_ALPHABETS_FUZZY_OPTIMISED`** matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">42 Main Street</td><td valign="top">44 Main Street</td><td valign="top">Yes - same as <code>ONLY_ALPHABETS_FUZZY</code>, numbers stripped, "Main Street" is identical</td></tr><tr><td valign="top">42 Main St</td><td valign="top">44 Main Street</td><td valign="top">Yes - same as <code>ONLY_ALPHABETS_FUZZY</code>, "Main St" vs "Main Street" scores a high fuzzy similarity</td></tr><tr><td valign="top">42 Main St</td><td valign="top">42 Oak St</td><td valign="top">No - same as <code>ONLY_ALPHABETS_FUZZY</code>, "Main St" vs "Oak St" too different alphabetically</td></tr><tr><td valign="top">[null]</td><td valign="top">Main Street</td><td valign="top">Yes - same as <code>ONLY_ALPHABETS_FUZZY</code>, a null/blank value on either side is an automatic match; add <code>NULL_OR_BLANK</code> if you want nulls excluded</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| 42 Main Street | 44 Main Street | Yes - same as `ONLY_ALPHABETS_FUZZY`, numbers stripped, "Main Street" is identical |
+| 42 Main St | 44 Main Street | Yes - same as `ONLY_ALPHABETS_FUZZY`, "Main St" vs "Main Street" scores a high fuzzy similarity |
+| 42 Main St | 42 Oak St | No - same as `ONLY_ALPHABETS_FUZZY`, "Main St" vs "Oak St" too different alphabetically |
+| [null] | Main Street | Yes - same as `ONLY_ALPHABETS_FUZZY`, a null/blank value on either side is an automatic match; add `NULL_OR_BLANK` if you want nulls excluded |
 
 ### When to use `ONLY_ALPHABETS_FUZZY_OPTIMISED`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
index 6428b466e..cddb65cb9 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
@@ -17,7 +17,15 @@ A null or blank value on either side is an automatic match.
 
 ### What `PINCODE` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Match?</th></tr></thead><tbody><tr><td valign="top">94102</td><td valign="top">94102-1234</td><td valign="top">Yes - both truncate to "94102"</td></tr><tr><td valign="top">94102</td><td valign="top">94103</td><td valign="top">No - "94102" vs "94103"</td></tr><tr><td valign="top">EC1A 1BB</td><td valign="top">EC1A1BB</td><td valign="top">No - neither string has a hyphen, so both pass through unsplit</td></tr><tr><td valign="top">110001</td><td valign="top">110001</td><td valign="top">Yes - Indian PIN code, identical</td></tr><tr><td valign="top">94102</td><td valign="top">941-02</td><td valign="top">No - "94102" vs "941" ("941-02" truncates to "941", discarding "02")</td></tr><tr><td valign="top">00-950</td><td valign="top">00-123</td><td valign="top">Yes - a false match. Both truncate to "00"; this is the Polish-postal-code failure case</td></tr><tr><td valign="top">[null]</td><td valign="top">94102</td><td valign="top">Yes - null/blank on either side auto-matches</td></tr></tbody></table>
+| Value A | Value B | Match? |
+|---|---|---|
+| 94102 | 94102-1234 | Yes - both truncate to "94102" |
+| 94102 | 94103 | No - "94102" vs "94103" |
+| EC1A 1BB | EC1A1BB | No - neither string has a hyphen, so both pass through unsplit |
+| 110001 | 110001 | Yes - Indian PIN code, identical |
+| 94102 | 941-02 | No - "94102" vs "941" ("941-02" truncates to "941", discarding "02") |
+| 00-950 | 00-123 | Yes - a false match. Both truncate to "00"; this is the Polish-postal-code failure case |
+| [null] | 94102 | Yes - null/blank on either side auto-matches |
 
 ### When to use `PINCODE`
 
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
index 51533e501..623622076 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
@@ -17,7 +17,14 @@ A null or blank value on either side is an automatic match (1.0).
 
 ### What `TEXT` matches and what it does not
 
-<table><thead><tr><th valign="top">Value A</th><th valign="top">Value B</th><th valign="top">Score</th><th valign="top">Notes</th></tr></thead><tbody><tr><td valign="top">Enterprise data management platform</td><td valign="top">Data management platform for enterprise</td><td valign="top">0.8 (Yes)</td><td valign="top">4 of 5 distinct words shared - only "for" is unmatched</td></tr><tr><td valign="top">Enterprise software solutions</td><td valign="top">Consumer hardware products</td><td valign="top">0.0 (No)</td><td valign="top">No shared words</td></tr><tr><td valign="top">Machine learning model training</td><td valign="top">Training machine learning model</td><td valign="top">1.0 (Yes)</td><td valign="top">Same four words, different order - word overlap is order-independent</td></tr><tr><td valign="top">ML model</td><td valign="top">Machine learning model</td><td valign="top">0.25 (Partial)</td><td valign="top">Only "model" is shared out of 4 distinct words across both - "ML" and "machine learning" don't overlap at the word level</td></tr><tr><td valign="top">[null]</td><td valign="top">Enterprise data platform</td><td valign="top">1.0 (Yes)</td><td valign="top">Null/blank on either side auto-matches</td></tr><tr><td valign="top">[empty string]</td><td valign="top">Enterprise data platform</td><td valign="top">1.0 (Yes)</td><td valign="top">Empty string is treated exactly like null</td></tr></tbody></table>
+| Value A | Value B | Score | Notes |
+|---|---|---|---|
+| Enterprise data management platform | Data management platform for enterprise | 0.8 (Yes) | 4 of 5 distinct words shared - only "for" is unmatched |
+| Enterprise software solutions | Consumer hardware products | 0.0 (No) | No shared words |
+| Machine learning model training | Training machine learning model | 1.0 (Yes) | Same four words, different order - word overlap is order-independent |
+| ML model | Machine learning model | 0.25 (Partial) | Only "model" is shared out of 4 distinct words across both - "ML" and "machine learning" don't overlap at the word level |
+| [null] | Enterprise data platform | 1.0 (Yes) | Null/blank on either side auto-matches |
+| [empty string] | Enterprise data platform | 1.0 (Yes) | Empty string is treated exactly like null |
 
 ### When to use `TEXT`
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/README.md b/docs/zingg-concepts/zingg-entity-resolution-platform/README.md
index 429899581..761d7c104 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/README.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/README.md
@@ -4,7 +4,9 @@ Zingg is an ML-powered entity resolution engine built to run where your data alr
 
 Three capabilities work together to handle the full problem:
 
-<table><thead><tr><th valign="top">Warehouse-native execution</th><th valign="top">Probabilistic + deterministic matching</th><th valign="top">Persistent identity graph</th></tr></thead><tbody><tr><td valign="top">Zingg runs inside Databricks, Microsoft<br>Fabric, Snowflake, GCP Dataproc, AWS Glue, and AWS EMR. Your data never<br>leaves your environment. No ETL pipelines. No external APIs. There is no separate infrastructure to operate. The same model that runs on 100,000 records scales to hundreds of millions using your existing Spark or Snowflake compute, without any architectural changes.</td><td valign="top"><p>Probabilistic matching is Zingg's default and available in the Community Edition. The ML model learns from your labeled pairs; 30 to 50 examples are enough to build a model calibrated<br>to your specific data and scores every candidate pair on multiple field-level features. It handles<br>typos, abbreviations, missing values, and format variations automatically.</p><p>Deterministic matching (Enterprise) adds hard rules for trusted identifiers.<br>When two records share the same SSN, tax ID, or email, Zingg treats them<br>as the same entity without consulting the ML model.<br><br>Both approaches run in a single flow, finding all possible matches.</p><p>→ <a href="deterministic-vs-probabilistic-matching.md">Deterministic vs Probabilistic<br>Matching</a></p></td><td valign="top"><p>Every resolved entity receives a <code>Zingg ID</code>, a globally unique, persistent GUID assigned in Enterprise that remains stable<br>across runs, incremental updates, and model changes.</p><p>Community produces a <code>Z Cluster</code><br>which is unique. Matching records share the same <code>Z_Cluster</code> that is reassigned each run.</p><p><br>Enterprise produces a <code>Zingg ID</code> you can store in downstream systems<br>with confidence.</p><p>The identity graph grows incrementally. New records are matched to existing clusters without rerunning on your full dataset.</p><p>→ <a href="../identity-graph.md">Identity Graph</a><br>→ <a href="../z-cluster-and-zingg-id.md">Z Cluster and Zingg ID</a></p></td></tr></tbody></table>
+| Warehouse-native execution | Probabilistic + deterministic matching | Persistent identity graph |
+|---|---|---|
+| Zingg runs inside Databricks, Microsoft Fabric, Snowflake, GCP Dataproc, AWS Glue, and AWS EMR. Your data never leaves your environment. No ETL pipelines. No external APIs. There is no separate infrastructure to operate. The same model that runs on 100,000 records scales to hundreds of millions using your existing Spark or Snowflake compute, without any architectural changes. | Probabilistic matching is Zingg's default and available in the Community Edition. The ML model learns from your labeled pairs; 30 to 50 examples are enough to build a model calibrated to your specific data and scores every candidate pair on multiple field-level features. It handles typos, abbreviations, missing values, and format variations automatically. Deterministic matching (Enterprise) adds hard rules for trusted identifiers. When two records share the same SSN, tax ID, or email, Zingg treats them as the same entity without consulting the ML model. Both approaches run in a single flow, finding all possible matches. → [Deterministic vs Probabilistic Matching](deterministic-vs-probabilistic-matching.md) | Every resolved entity receives a `Zingg ID`, a globally unique, persistent GUID assigned in Enterprise that remains stable across runs, incremental updates, and model changes. Community produces a `Z Cluster` which is unique. Matching records share the same `Z_Cluster` that is reassigned each run. Enterprise produces a `Zingg ID` you can store in downstream systems with confidence. The identity graph grows incrementally. New records are matched to existing clusters without rerunning on your full dataset. → [Identity Graph](../identity-graph.md) → [Z Cluster and Zingg ID](../z-cluster-and-zingg-id.md) |
 
 This is the combination that makes enterprise-scale entity resolution computationally feasible and practically maintainable.
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md b/docs/zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md
index d7ec4ff4d..6b50945a5 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md
@@ -20,7 +20,7 @@ _You do not write rules. You label examples. Zingg learns the rest._
 
 ### Deterministic matching
 
-Deterministic matching is only available in the Enterprise Edition. It lets you define hard rules for cases where certain field combinations should always produce a match, regardless of the probabilistic score. When your data contains reliable unique identifiers, a national ID, an email, or a combination like first name, email plus date of birth, determinstic matching can bring them together. Zingg Enterprise can apply any combination of deterministic rules like (ssn) or (email and dob) or (passport number and dob).&#x20;
+Deterministic matching is only available in the Enterprise Edition. It lets you define hard rules for cases where certain field combinations should always produce a match, regardless of the probabilistic score. When your data contains reliable unique identifiers, a national ID, an email, or a combination like first name, email plus date of birth, determinstic matching can bring them together. Zingg Enterprise can apply any combination of deterministic rules like (ssn) or (email and dob) or (passport number and dob). 
 
 ### How Zingg matches probabilistically AND deterministically
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/incremental-run.md b/docs/zingg-concepts/zingg-entity-resolution-platform/incremental-run.md
index e5c89971c..b509ae609 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/incremental-run.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/incremental-run.md
@@ -7,7 +7,7 @@ tags:
 
 # Incremental Flow
 
-Data in enteprise systems gets regularly updated. The business expands, and new customer records come in. Entities change - people move, get new phones, register new businesses. Some systems cease and records get deleted. Matching once is powerful, but it ceases to be enough in such cases. A full rematch is costly, and loses state.&#x20;
+Data in enteprise systems gets regularly updated. The business expands, and new customer records come in. Entities change - people move, get new phones, register new businesses. Some systems cease and records get deleted. Matching once is powerful, but it ceases to be enough in such cases. A full rematch is costly, and loses state. 
 
 The process of updating the identity graph with new, changed, or deleted records without re-running the full match across the entire dataset is known as incremental flow in Zingg. Zingg incorporates incoming records into existing clusters, handles cluster merges and unmerges automatically, generates new Zingg IDs for records that do not match any existing cluster, and preserves human-approved decisions so they are not overridden.
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/streaming-vs-batch-entity-resolution.md b/docs/zingg-concepts/zingg-entity-resolution-platform/streaming-vs-batch-entity-resolution.md
index e7e2291cd..afbe4956c 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/streaming-vs-batch-entity-resolution.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/streaming-vs-batch-entity-resolution.md
@@ -42,13 +42,13 @@ The requirement underneath all of these is the same: **real-time decisioning req
 
 ### Why not just make batch faster?
 
-It's tempting to think streaming ER is just batch ER running on a shorter schedule. It isn't, for a structural reason: architectures are built differently in data platforms.&#x20;
+It's tempting to think streaming ER is just batch ER running on a shorter schedule. It isn't, for a structural reason: architectures are built differently in data platforms. 
 
 Streaming entity resolution is different engineering problem (stateful stream processing, incremental identity graphs) rather than a smaller version of the same one.
 
 ### Why not real time?
 
-Warehouses and datalakes are still not ready for real time design patterns. While there is promising movement towards LTAP/HTAP with Lakebase and Postgres suppoer in Snowflake, we are watching this space keenly and will build something when the technology is right. Going out of the warehouse and supporting real time is one option, but that becomes a separate source of truth with its own ETL and governance. Hence it gets limited to single departments and fails to become the universal source of truth. &#x20;
+Warehouses and datalakes are still not ready for real time design patterns. While there is promising movement towards LTAP/HTAP with Lakebase and Postgres suppoer in Snowflake, we are watching this space keenly and will build something when the technology is right. Going out of the warehouse and supporting real time is one option, but that becomes a separate source of truth with its own ETL and governance. Hence it gets limited to single departments and fails to become the universal source of truth.  
 
 ### Choosing an architecture in Zingg
 
diff --git a/docs/zingg-concepts/zingg-entity-resolution-platform/the-problems-zingg-is-built-to-solve-and-why-they-are-harder-than-it-looks.md b/docs/zingg-concepts/zingg-entity-resolution-platform/the-problems-zingg-is-built-to-solve-and-why-they-are-harder-than-it-looks.md
index 5939627c4..912a3c7e7 100644
--- a/docs/zingg-concepts/zingg-entity-resolution-platform/the-problems-zingg-is-built-to-solve-and-why-they-are-harder-than-it-looks.md
+++ b/docs/zingg-concepts/zingg-entity-resolution-platform/the-problems-zingg-is-built-to-solve-and-why-they-are-harder-than-it-looks.md
@@ -11,7 +11,7 @@ Entity resolution at scale is not a data quality problem. It is an algorithmic c
 | **The N² comparison problem**                                        | <p>Naive entity resolution compares every record against every other record. With N records, that produces N × (N-1) / 2 comparisons.</p><ul><li>At 10,000 records: ~50 million comparisons.</li><li>At 1 million records: ~500 billion comparisons.</li><li>At 10 million records: ~50 trillion comparisons.</li></ul><p>No rules engine can evaluate 50 trillion pairs. No team can review them. And no cloud compute budget can sustain running that comparison at production cadence - weekly, daily, or on arrival of new records.This is not a hardware problem. It is a structural one. The comparison space grows quadratically with dataset size, and linear scaling of compute cannot outrun quadratic growth.Zingg's blocking model is the solution. Before any similarity comparison runs, the blocking model groups records into candidate buckets using learned field heuristics. Only records within the same bucket are ever compared. Typical Zingg comparison coverage is 0.05% to 1% of the full problem space - without losing the matching pairs that matter.</p>                                                                                                                                  |
 | **The variation problem - why rules always break**                   | Entities do not appear consistently across systems. "IBM", "I.B.M.", and "International Business Machines" are the same company. "Jon Smith" and "Jonathan Smith" may be the same person. "42 Main St" and "42 Main Street, Apt 3B" are likely the same address.A rule-based approach requires you to enumerate every possible variation for each field. For names alone, the number of possible abbreviations, abbreviation types, and transliteration patterns is unbounded. For addresses, it is worse. For company names across geographies and time periods, it is not practically solvable with rules.Zingg's similarity model handles variation by computing multiple field-level features, character differences, string lengths, common transpositions, prefixes, suffix overlaps, and combining them through a classifier trained on your labeled pairs.The classifier learns the variation patterns in your specific data. It does not need you to enumerate them in advance.                                                                                                                                                                                                                                |
 | **The confidence problem - why binary match/no-match is not enough** | <p>A rule either fires or it does not. It gives you a binary answer: match or no match. Production entity resolution requires more than that. Some clusters are high confidence - strong matches across multiple fields that can go straight to automated golden record creation. Others are lower confidence plausible matches that a domain expert should review before merging. Others are borderline pairs that look similar but are probably different entities.Without a graded confidence signal, every match decision has to be treated the same way. You either automate everything and accept errors or review everything manually and abandon the scale benefit entirely. Zingg's similarity model produces a score between 0 and 1 for every candidate pair. That score drives <code>Z_MINSCORE</code> and <code>Z_MAXSCORE</code> on every cluster in your output - so you can route high-confidence clusters to automated processing and low-confidence clusters to a stewardship queue.</p><p></p><p><strong>Read more</strong>:</p><ul><li>​<a href="../../interpreting-results/interpret-output-scores.md">Interpret Output Scores</a>​</li></ul>                                                      |
-| **The pairs are not enough problem**                                 | <p></p><p>After the similarity model scores every candidate pair, a graph algorithm groups them into complete clusters using transitive closure.</p><p>If Record A matches Record B, and Record B matches Record C, the algorithm concludes that A, B, and C all represent the same entity and groups them into a single cluster, even if A and C were never directly compared.</p><p>This is what turns a list of scored pairs into a usable identity graph. Every resolved entity becomes a node. Every cluster is an entity resolved across all its representations.</p><p>In Community, clusters are assigned a <code>Z Cluster</code> that is non-persistent between runs. In Enterprise, each cluster is assigned a persistent <code>Zingg ID</code> (GUID) that remains stable across runs, incremental updates, and model changes.</p><div data-gb-custom-block data-tag="hint" data-style="success" data-icon="right-long" class="hint hint-success"><p><strong>Read more</strong>:</p><ul><li><a href="../identity-graph.md">Identity Graph</a></li><li><a href="../z-cluster-and-zingg-id.md">Z Cluster ID vs Zingg ID</a></li><li><a href="/broken/pages/9QpDFW20AMt0UJ4cEW6b">Zingg ID</a></li></ul></div> |
+| **The pairs are not enough problem**                                 | <p></p><p>After the similarity model scores every candidate pair, a graph algorithm groups them into complete clusters using transitive closure.</p><p>If Record A matches Record B, and Record B matches Record C, the algorithm concludes that A, B, and C all represent the same entity and groups them into a single cluster, even if A and C were never directly compared.</p><p>This is what turns a list of scored pairs into a usable identity graph. Every resolved entity becomes a node. Every cluster is an entity resolved across all its representations.</p><p>In Community, clusters are assigned a <code>Z Cluster</code> that is non-persistent between runs. In Enterprise, each cluster is assigned a persistent <code>Zingg ID</code> (GUID) that remains stable across runs, incremental updates, and model changes.</p><div data-gb-custom-block data-tag="hint" data-style="success" data-icon="right-long" class="hint hint-success"><p><strong>Read more</strong>:</p><ul><li><a href="../identity-graph.md">Identity Graph</a></li><li><a href="../z-cluster-and-zingg-id.md">Z Cluster ID vs Zingg ID</a></li><li><a href="../z-cluster-and-zingg-id.md">Zingg ID</a></li></ul></div> |
 | **Managing addition, updation and deletion of records**              | <p>Production entity resolution rarely sees a static dataset. New records arrive daily; sometimes hourly. The naive approach is to rerun the full match each time new data comes in. This breaks at scale for two reasons. First, every re-run produces fresh cluster IDs. Downstream systems that store the cluster ID as a customer or entity key break every time the match is re-run.</p><p></p><p>Zingg Enterprise's <code>runIncremental</code> phase handles this. New records are evaluated against the existing identity graph. Records matching an existing cluster inherit its Zingg ID. Records that do not match any existing cluster receive a new Zingg ID. The existing graph stays intact. Downstream systems keep working.<br><br>Second, re-running the full match is computationally expensive and slows down your pipelines. Doing it every time a thousand new records arrive is not the best way to process data.<br><br><strong>Read more</strong>:</p><ul><li>​<a href="../../running-zingg/run-incremental-matching.md">Run Incremental Matching</a>​</li></ul>                                                                                                                               |
 | **The data drift problem - why models decay**                        | <p>Data patterns change. New source systems are added. Naming conventions shift. A model trained on last year's data may perform poorly on this year's records, not because the model was wrong, but because the variation distribution in your data changed. <br><br></p><p>Most systems have no mechanism to detect or adapt to these changes. Zingg Enterprise's compare model results feature (<code>diff</code> phase) lets you benchmark a retrained model against your current production model before deploying. So, you can see exactly what changed before the change goes live. Zingg Enterprise also provides reassign feature, so you can continue with existing <code>ZINGG ID</code>s even while upgrading models.<strong>Read more</strong>:</p><ul><li>​<a href="../../running-zingg/compare-model-results.md">Compare Model Results</a>​</li><li>​<a href="../../running-zingg/reassign-zingg-id.md">Reassign Zingg ID</a>​</li></ul>                                                                                                                                                                                                                                                                 |
 | **The governance gap**                                               | Explaining how records matched or why they did not match is critical for audit and compliance, especially in regulated industries. Zingg Enterprise comes with a whole suit of explanations at the record and cluster level to help stakeholders understand the matching.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
diff --git a/docs/zingg-concepts/zingg-phases.md b/docs/zingg-concepts/zingg-phases.md
index 8612bf7dc..bfb4b650d 100644
--- a/docs/zingg-concepts/zingg-phases.md
+++ b/docs/zingg-concepts/zingg-phases.md
@@ -4,13 +4,13 @@ description: Steps in the entity resolution pipeline
 
 # Zingg Phases
 
-Zingg programs run different aspects of the entity resolution pipeline. Each phase covers one aspect of the pipeline. Here are the phases Zingg defines.&#x20;
+Zingg programs run different aspects of the entity resolution pipeline. Each phase covers one aspect of the pipeline. Here are the phases Zingg defines. 
 
 <details>
 
 <summary><strong><code>findTrainingData</code></strong></summary>
 
-Scans your dataset and selects the most informative candidate record pairs for labeling - edge cases where the model has the most to learn. Candidate pairs are written within the warehouse/lakehouse based on your configuration.&#x20;
+Scans your dataset and selects the most informative candidate record pairs for labeling - edge cases where the model has the most to learn. Candidate pairs are written within the warehouse/lakehouse based on your configuration. 
 
 </details>
 
@@ -18,7 +18,7 @@ Scans your dataset and selects the most informative candidate record pairs for l
 
 <summary><strong><code>label</code></strong></summary>
 
-Loads the candidate pairs from `findTrainingData` and presents them in a labeling widget. You mark each pair as Match, No Match, or Uncertain.&#x20;
+Loads the candidate pairs from `findTrainingData` and presents them in a labeling widget. You mark each pair as Match, No Match, or Uncertain. 
 
 </details>
 
diff --git a/docs/zingg-python-api/community-python-api.md b/docs/zingg-python-api/community-python-api.md
index fa86fee03..fb09f993d 100644
--- a/docs/zingg-python-api/community-python-api.md
+++ b/docs/zingg-python-api/community-python-api.md
@@ -25,11 +25,22 @@ pip install zingg
 
 #### **`zingg.client` module**
 
-<table><thead><tr><th width="245.59375" valign="top">Class</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>Zingg</code></td><td valign="top">The Zingg execution client. Pass it <code>args</code> and <code>options</code>, call <code>initAndExecute()</code>.</td></tr><tr><td valign="top"><code>ZinggWithSpark</code></td><td valign="top">Same as <code>Zingg</code> but for use when an existing Spark session is already present (e.g. in a Databricks or Fabric notebook).</td></tr><tr><td valign="top"><code>Arguments</code></td><td valign="top">Configuration object holding the model ID, Zingg directory, partition count, field definitions, and pipes.</td></tr><tr><td valign="top"><code>ClientOptions</code></td><td valign="top">Holds the phase name. Pass to the Zingg execution client.</td></tr><tr><td valign="top"><code>FieldDefinition</code></td><td valign="top">Defines a single field with its name, data type, and match type.</td></tr></tbody></table>
+| Class | Purpose |
+|---|---|
+| `Zingg` | The Zingg execution client. Pass it `args` and `options`, call `initAndExecute()`. |
+| `ZinggWithSpark` | Same as `Zingg` but for use when an existing Spark session is already present (e.g. in a Databricks or Fabric notebook). |
+| `Arguments` | Configuration object holding the model ID, Zingg directory, partition count, field definitions, and pipes. |
+| `ClientOptions` | Holds the phase name. Pass to the Zingg execution client. |
+| `FieldDefinition` | Defines a single field with its name, data type, and match type. |
 
 #### **`zingg.pipes` module**
 
-<table><thead><tr><th width="249.9453125" valign="top">Class</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>Pipe</code></td><td valign="top">Base class for any input or output. Use for generic JDBC, custom formats, or any source where the specialised classes do not fit.</td></tr><tr><td valign="top"><code>CsvPipe</code></td><td valign="top">Read or write CSV files.</td></tr><tr><td valign="top"><code>BigQueryPipe</code></td><td valign="top">Read from or write to Google BigQuery.</td></tr><tr><td valign="top"><code>SnowflakePipe</code></td><td valign="top">Read from or write to Snowflake (via Spark connector).</td></tr></tbody></table>
+| Class | Purpose |
+|---|---|
+| `Pipe` | Base class for any input or output. Use for generic JDBC, custom formats, or any source where the specialised classes do not fit. |
+| `CsvPipe` | Read or write CSV files. |
+| `BigQueryPipe` | Read from or write to Google BigQuery. |
+| `SnowflakePipe` | Read from or write to Snowflake (via Spark connector). |
 
 {% hint style="success" icon="right-long" %}
 Full auto-generated method signatures for every class above are at the [Zingg Python OSS API reference on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonOss/markdown/zingg.client.md).
diff --git a/docs/zingg-python-api/enterprise-zinggec-python-api.md b/docs/zingg-python-api/enterprise-zinggec-python-api.md
index 380ff7336..e0a34403b 100644
--- a/docs/zingg-python-api/enterprise-zinggec-python-api.md
+++ b/docs/zingg-python-api/enterprise-zinggec-python-api.md
@@ -39,7 +39,14 @@ pip install zinggEC
 
 #### **`zinggEC.enterprise.common` package**
 
-<table><thead><tr><th width="203.640625" valign="top">Module</th><th width="222.890625" valign="top">Classes</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>ApproverArguments</code></td><td valign="top"><code>ApproverArguments</code></td><td valign="top">Cluster approval workflow - set approval query and destination for human-reviewed clusters</td></tr><tr><td valign="top"><code>IncrementalArguments</code></td><td valign="top"><code>IncrementalArguments</code></td><td valign="top">Incremental matching - incremental data, deleted data, delete action, output temp directory, parent args</td></tr><tr><td valign="top"><code>MappingMatchType</code></td><td valign="top"><code>MappingMatchType</code></td><td valign="top">User-supplied lookup file for nickname/abbreviation matching</td></tr><tr><td valign="top"><code>epipes</code></td><td valign="top"><code>ECsvPipe</code>, <code>EPipe</code>, <code>InMemoryPipe</code>, <code>UCPipe</code></td><td valign="top">Enterprise pipe types including CSV, base pipe, in-memory DataFrame, and Unity Catalog table</td></tr><tr><td valign="top"><code>EArguments</code></td><td valign="top"><code>EArguments</code>, <code>DeterministicMatching</code></td><td valign="top">Enterprise arguments object plus deterministic matching rules</td></tr><tr><td valign="top"><code>EFieldDefinition</code></td><td valign="top"><code>EFieldDefinition</code></td><td valign="top">Enterprise field definition with primary key support and mapping match type</td></tr></tbody></table>
+| Module | Classes | Purpose |
+|---|---|---|
+| `ApproverArguments` | `ApproverArguments` | Cluster approval workflow - set approval query and destination for human-reviewed clusters |
+| `IncrementalArguments` | `IncrementalArguments` | Incremental matching - incremental data, deleted data, delete action, output temp directory, parent args |
+| `MappingMatchType` | `MappingMatchType` | User-supplied lookup file for nickname/abbreviation matching |
+| `epipes` | `ECsvPipe`, `EPipe`, `InMemoryPipe`, `UCPipe` | Enterprise pipe types including CSV, base pipe, in-memory DataFrame, and Unity Catalog table |
+| `EArguments` | `EArguments`, `DeterministicMatching` | Enterprise arguments object plus deterministic matching rules |
+| `EFieldDefinition` | `EFieldDefinition` | Enterprise field definition with primary key support and mapping match type |
 
 {% hint style="success" icon="right-long" %}
 Full auto-generated method signatures for every class are at the [Zingg Enterprise Common Python API reference on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonEC/markdown/zinggEC.md).
diff --git a/docs/zingg-python-api/enterprise-zingges-python-api.md b/docs/zingg-python-api/enterprise-zingges-python-api.md
index 09fdf178a..ea3bd2898 100644
--- a/docs/zingg-python-api/enterprise-zingges-python-api.md
+++ b/docs/zingg-python-api/enterprise-zingges-python-api.md
@@ -40,7 +40,9 @@ pip install zinggES
 
 #### **`zinggES.enterprise.spark` package**
 
-<table><thead><tr><th valign="top">Module</th><th valign="top">Classes</th><th valign="top">Purpose</th></tr></thead><tbody><tr><td valign="top"><code>ESparkClient</code></td><td valign="top"><code>EZingg</code>, <code>EZinggWithSpark</code></td><td valign="top">Enterprise Plus Spark execution clients</td></tr></tbody></table>
+| Module | Classes | Purpose |
+|---|---|---|
+| `ESparkClient` | `EZingg`, `EZinggWithSpark` | Enterprise Plus Spark execution clients |
 
 `EZingg` is the standard Enterprise Plus client. `EZinggWithSpark` is for environments where a Spark session already exists (Databricks, Fabric notebooks) - it reuses that session instead of creating a new one.
 
diff --git a/docs/zingg-python-api/working-with-python.md b/docs/zingg-python-api/working-with-python.md
index 75c149e27..e6815130c 100644
--- a/docs/zingg-python-api/working-with-python.md
+++ b/docs/zingg-python-api/working-with-python.md
@@ -16,7 +16,11 @@ To run on a local machine, install Zingg from the release before running Zingg P
 
 #### Choose your edition
 
-<table><thead><tr><th width="237.09375" valign="top">Edition</th><th width="127.88671875" valign="top">Package</th><th valign="top">Use when</th></tr></thead><tbody><tr><td valign="top"><strong>Community (Open Source)</strong></td><td valign="top"><code>zingg</code></td><td valign="top">Free, open-source Zingg for any Spark deployment</td></tr><tr><td valign="top"><strong>Enterprise (ZinggEC)</strong></td><td valign="top"><code>zinggEC</code></td><td valign="top">Enterprise Lite or Enterprise — adds deterministic matching, blocking strategy, primary key, pass-through, mapping match types, incremental, output stats</td></tr><tr><td valign="top"><strong>Enterprise Plus (ZinggES)</strong></td><td valign="top"><code>zinggES</code></td><td valign="top">Adds the Spark client (<code>EZingg</code>, <code>EZinggWithSpark</code>) used by Enterprise Plus distributions</td></tr></tbody></table>
+| Edition | Package | Use when |
+|---|---|---|
+| **Community (Open Source)** | `zingg` | Free, open-source Zingg for any Spark deployment |
+| **Enterprise (ZinggEC)** | `zinggEC` | Enterprise Lite or Enterprise — adds deterministic matching, blocking strategy, primary key, pass-through, mapping match types, incremental, output stats |
+| **Enterprise Plus (ZinggES)** | `zinggES` | Adds the Spark client (`EZingg`, `EZinggWithSpark`) used by Enterprise Plus distributions |
 
 {% hint style="success" icon="right-long" %}
 **Read more**: Pick your edition and follow the sub-page below:

From f0786e61e0964720411ced526c1b36e3936e8da8 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 13:32:35 +0530
Subject: [PATCH 5/9] docs: restyle landing, concepts, and running-zingg
 sections for issue #1363

---
 .../running-zingg/build-and-save-the-model.md |  6 +--
 docs/running-zingg/cluster-approval.md        |  4 +-
 docs/running-zingg/compare-model-results.md   |  6 +--
 docs/running-zingg/configure-zingg.md         | 38 +++++++++----------
 .../create-training-data/README.md            | 10 ++---
 .../label-training-pairs.md                   | 14 +++----
 .../create-training-data/verify-blocking.md   | 10 ++---
 docs/running-zingg/experience-zingg.md        | 10 ++---
 .../generate-model-documentation.md           |  8 ++--
 docs/running-zingg/install-zingg.md           | 16 ++++----
 docs/running-zingg/knowledge-graph.md         |  6 +--
 docs/running-zingg/link-across-datasets.md    | 10 ++---
 docs/running-zingg/lookup-data.md             |  4 +-
 docs/running-zingg/pass-through.md            |  6 +--
 docs/running-zingg/quick-start-docker.md      | 28 +++++++++-----
 docs/running-zingg/reassign-zingg-id.md       |  6 +--
 .../running-zingg/run-incremental-matching.md | 10 ++---
 docs/running-zingg/run-the-match-phase.md     | 10 ++---
 .../set-deterministic-matching-conditions.md  |  6 +--
 docs/running-zingg/step-by-step-guide.md      | 32 ++++++++--------
 docs/running-zingg/update-label.md            |  6 +--
 21 files changed, 127 insertions(+), 119 deletions(-)

diff --git a/docs/running-zingg/build-and-save-the-model.md b/docs/running-zingg/build-and-save-the-model.md
index c5752a736..5784f410a 100644
--- a/docs/running-zingg/build-and-save-the-model.md
+++ b/docs/running-zingg/build-and-save-the-model.md
@@ -4,7 +4,7 @@ description: >-
   training data and save it for reuse.
 ---
 
-# Build and Save the Model
+# 🧪 Build and Save the Model
 
 The training phase builds up the Zingg models using the training data from your label sessions and writes them to `zinggDir/modelId` as specified in your config.
 
@@ -18,11 +18,11 @@ Once saved, reuse the same `modelId` in all subsequent phases - `match`, `link`,
 
 If you retrain with a new model, use `Compare Model Results (diff phase)` in Enterprise to benchmark the new model against the current one before deploying. Zingg shows you exactly which clusters changed, merged, or split between the two models.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Model saved to: `zinggDir/modelId`
 
 Use the same `modelId` when running `match`, `link`, or `runIncremental` to apply this trained model.
diff --git a/docs/running-zingg/cluster-approval.md b/docs/running-zingg/cluster-approval.md
index b74fe5b1d..b5af5bee5 100644
--- a/docs/running-zingg/cluster-approval.md
+++ b/docs/running-zingg/cluster-approval.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Cluster Approval
+# ✅ Cluster Approval
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise only. Manual review and sign-off of matched clusters before committing to the identity graph. Approved decisions are preserved across incremental runs and never overridden.
 {% endhint %}
 
diff --git a/docs/running-zingg/compare-model-results.md b/docs/running-zingg/compare-model-results.md
index 9e3a73ff1..afef6fd10 100644
--- a/docs/running-zingg/compare-model-results.md
+++ b/docs/running-zingg/compare-model-results.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Compare Model Results
+# 📊 Compare Model Results
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 **Enterprise** **Plus** only. Comparing model results is available in Zingg Enterprise Plus (ZinggES).\
 Not available in Community, Enterprise Lite, or Enterprise.
 {% endhint %}
@@ -29,7 +29,7 @@ Retrained an existing model with more labeled data and want to confirm the updat
 * Run an A/B test between two modeling strategies and need a quantitative basis for choosing one over the other.
 * Changed blocking strategy settings and want to measure the effect on cluster quality before committing to production
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:**
 
 * Model workflow context - [Run the match phase](run-the-match-phase.md)
diff --git a/docs/running-zingg/configure-zingg.md b/docs/running-zingg/configure-zingg.md
index bdf5e4534..223837c65 100644
--- a/docs/running-zingg/configure-zingg.md
+++ b/docs/running-zingg/configure-zingg.md
@@ -4,20 +4,20 @@ description: >-
   subsequent Zingg phase depends on.
 ---
 
-# Configure Zingg
+# ⚙️ Configure Zingg
 
 Configuring Zingg is the first step in every Zingg workflow. This page follows notebook 01 step by step; defining your fields, configuring your pipes, and creating the arguments object that all subsequent phases inherit.
 
 Complete this page before running any other phase. Every phase reads the configuration set here:
 
-* `findTrainingData` → \[[Create Training Data](create-training-data/)]
-* `label` → \[[Label Training Pairs](create-training-data/label-training-pairs.md)]
-* `train` → \[[Build and Save the Model](build-and-save-the-model.md)]
-* `match` → \[[Run the Match Phase](run-the-match-phase.md)]
-* `link` → \[[Link Across Datasets](link-across-datasets.md)]
-* `runIncremental` → \[[Run Incremental Matching](run-incremental-matching.md)]
+* `findTrainingData` → [Create Training Data](create-training-data/)
+* `label` → [Label Training Pairs](create-training-data/label-training-pairs.md)
+* `train` → [Build and Save the Model](build-and-save-the-model.md)
+* `match` → [Run the Match Phase](run-the-match-phase.md)
+* `link` → [Link Across Datasets](link-across-datasets.md)
+* `runIncremental` → [Run Incremental Matching](run-incremental-matching.md)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Full parameter reference with all config options and schema → [Configuration Schema](../reference/configuration-schema.md)
 {% endhint %}
 
@@ -48,7 +48,7 @@ args.setNumPartitions(4)
 args.setLabelDataSampleSize(0.5)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `setModelId` - unique name for this model.
 
 `setZinggDir` - where Zingg writes model files and training data.
@@ -172,7 +172,7 @@ args.setFieldDefinition(fieldDefs)
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Match types reference - [Match types](../zingg-concepts/zingg-configuration/field-definition/match-types/) | [Configuration schema](../reference/configuration-schema.md)
 {% endhint %}
 
@@ -241,7 +241,7 @@ If you do not want to pass sensitive values such as passwords through the config
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="plug" %}
 **Read more**: For all supported connector types and formats - [Connect Your Data](../connect-your-data/pipes-and-data-connections.md)
 {% endhint %}
 {% endtab %}
@@ -368,7 +368,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise requires a primary key field for `runIncremental`. Mark the primary key field by calling `recId.setPrimaryKey(True)` if you plan to use incremental matching.
 {% endhint %}
 
@@ -427,7 +427,7 @@ args.setOutput(outputPipe)
 
 ### Step 5: Deterministic matching (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Deterministic matching - **Enterprise** only.
 
 Skip this step if you only need probabilistic matching.
@@ -482,7 +482,7 @@ args.setDeterministicMatchingCondition(
 
 ### Step 6: Output stats (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Output stats - **Enterprise** only.
 
 Skip this step if you do not need run statistics.
@@ -520,7 +520,7 @@ args.setOutputStats(statsPipe)
 
 ### Step 7: Pass Through (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Pass Through - **Enterprise** only.
 
 Skip this step if you do not need to exclude records from matching.
@@ -607,7 +607,7 @@ args.setPassthroughExpr("fname = 'matilda'")
 	]
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise requires a primary key field for `runIncremental`. Mark the primary key field by adding `"primaryKey": "true"` if you plan to use incremental matching.
 {% endhint %}
 
@@ -634,7 +634,7 @@ Enterprise requires a primary key field for `runIncremental`. Mark the primary k
 
 ### Step 4: Deterministic matching (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Deterministic matching - **Enterprise** only.
 
 Skip this step if you only need probabilistic matching.
@@ -661,7 +661,7 @@ Skip this step if you only need probabilistic matching.
 
 ### Step 5: Output stats (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Output stats - **Enterprise** only.
 
 Skip this step if you do not need run statistics.
@@ -685,7 +685,7 @@ Configures where Zingg writes statistics for the `match` and `runIncremental` ph
 
 ### Step 6: Pass Through (optional)
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Pass Through - **Enterprise** only.
 
 Excludes specific records from matching while still including them in output with their own `Zingg ID`.
diff --git a/docs/running-zingg/create-training-data/README.md b/docs/running-zingg/create-training-data/README.md
index 2c8024d34..b347dbff2 100644
--- a/docs/running-zingg/create-training-data/README.md
+++ b/docs/running-zingg/create-training-data/README.md
@@ -4,13 +4,13 @@ description: >-
   The first active step in building your Zingg model.
 ---
 
-# Create Training Data
+# 🧪 Create Training Data
 
 The `findTrainingData` phase prompts Zingg to search for edge cases in your data-record pairs that are informative for learning. Zingg selects these judiciously so that your labeling effort is minimized and models can be built quickly.
 
 Run `findTrainingData` first, then run the `label` phase. Repeat this cycle until you have enough labeled pairs.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Zingg selects the most informative pairs from your data not random samples. Label until all field types and data variation patterns in your schema are represented. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -63,7 +63,7 @@ Your training data must include:
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Even when supplying pre-existing training data, it is advisable to run `findTrainingData` and `label` for a few rounds to tune Zingg with the patterns it needs to learn independently.
 {% endhint %}
 {% endtab %}
@@ -89,7 +89,7 @@ zingg.initAndExecute()
 ./scripts/zingg.sh --phase findTrainingData --conf config.json
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Candidate pairs are written to `zinggDir/modelId`. Run the label phase next to review and label these pairs.
 {% endhint %}
 {% endtab %}
@@ -109,7 +109,7 @@ Zingg on Snowflake can be run either from a local terminal via the CLI, or nativ
 
 The `snowflake.properties` file supplies the connection details Zingg needs to reach your Snowflake account and run against your warehouse from outside Snowflake.
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Candidate pairs are written to `zinggDir/modelId`.
 {% endhint %}
 
diff --git a/docs/running-zingg/create-training-data/label-training-pairs.md b/docs/running-zingg/create-training-data/label-training-pairs.md
index d59fa039a..75de93dc3 100644
--- a/docs/running-zingg/create-training-data/label-training-pairs.md
+++ b/docs/running-zingg/create-training-data/label-training-pairs.md
@@ -4,7 +4,7 @@ description: >-
   human feedback step that teaches Zingg what a match looks like in your data.
 ---
 
-# Label Training Pairs
+# 🧪 Label Training Pairs
 
 The `label` phase opens an interactive layer where you review the candidate pairs found by `findTrainingData` and mark each pair. This is the only step in the Zingg workflow that requires human input. No ML knowledge is needed, just your domain understanding of whether two records represent the same real-world entity.
 
@@ -48,11 +48,11 @@ zingg.initAndExecute()
 ./scripts/zingg.sh --phase label --conf config.json --showConcise=true
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `--showConcise=true` is optional. It only shows fields which are not `DONT_USE`, making the labelling session cleaner when you have many fields.
 {% endhint %}
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 Need to correct a pair you already labeled? See [Update Label](../update-label.md).
 {% endhint %}
 {% endtab %}
@@ -72,11 +72,11 @@ zingg.initAndExecute()
 ./scripts/zingg.sh --phase label --conf config.json --showConcise=true
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `--showConcise=true` is optional. It only shows fields which are not `DONT_USE`, making the labelling session cleaner when you have many fields.
 {% endhint %}
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Notebook provides a visual widget showing one pair at a time with Match, No Match, and Can't Say buttons.
 {% endhint %}
 {% endtab %}
@@ -88,7 +88,7 @@ Zingg on Snowflake can be run either from a local terminal via the CLI, or nativ
 
 #### CLI (local terminal)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `--showConcise=true` is optional. It only shows fields which are not `DONT_USE`, making the labelling session cleaner when you have many fields.
 {% endhint %}
 
@@ -127,7 +127,7 @@ Once you're done labeling, stop the compute pool to release resources.
 ALTER COMPUTE POOL CONTAINER_ZINGG_POOL STOP ALL;
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Notebook provides a visual widget showing one pair at a time with Match, No Match, and Can't Say buttons.
 {% endhint %}
 {% endtab %}
diff --git a/docs/running-zingg/create-training-data/verify-blocking.md b/docs/running-zingg/create-training-data/verify-blocking.md
index ca9230f35..4b0186299 100644
--- a/docs/running-zingg/create-training-data/verify-blocking.md
+++ b/docs/running-zingg/create-training-data/verify-blocking.md
@@ -7,7 +7,7 @@ tags:
     primary: true
 ---
 
-# Verify Blocking
+# 🧪 Verify Blocking
 
 The blocking model filters your dataset before any similarity comparisons happen. Records in different blocks are never compared. This is what makes Zingg scalable, but it also means that if the blocking model puts two matching records in different blocks, those records will never be matched no matter how well the similarity model is trained.
 
@@ -28,7 +28,7 @@ The `verifyBlocking` phase produces two output directories under `zinggDir/model
 | `counts`       | Record count per block. Shows how many records are in each block. Very large blocks indicate poor blocking specificity.                                  |
 | `blockSamples` | The top 10% of records associated with each block. Use this to understand which records are being grouped together and whether the groupings make sense. |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: To run the `verifyBlocking` phase and inspect output coverage→ [Verify Blocking](verify-blocking.md)
 {% endhint %}
 
@@ -86,7 +86,7 @@ influence matching.
 3. Check whether missed pairs share a common characteristic. If all missed pairs have empty values in a key field, empty fields cannot contribute to blocking. Either remove that field from your blocking config or improve data completeness upstream.
 4. If standard blocking consistently misses a specific pattern in your data, consider custom blocking functions.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * [Label Training Pairs](label-training-pairs.md) - how to add more training data
@@ -95,7 +95,7 @@ influence matching.
 * [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md) - define custom blocking functions
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Run `verifyBlocking` in two situations:
 
 1. **Before labeling** - to confirm your blocking is covering expected matches before you invest in training data
@@ -171,7 +171,7 @@ If `verifyBlocking` shows that many known matching pairs are not being blocked t
 2. **Check your field match types.** Fields set to `DONT_USE` are excluded from blocking. If a key identity field is set to `DONT_USE`, matching pairs that differ on other fields may end up in different blocks.
 3. **Try the `WIDER` blocking strategy** (Enterprise only) if large blocks suggest the tree is over-relying on one or two fields → [Blocking Strategies: DEFAULT vs WIDER](../../tuning/blocking-strategy.md)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Consider custom blocking functions** for specialised data patterns → [Blocking Model](../../zingg-concepts/how-zingg-learns/zingg-models/blocking-model.md)
 
 **Read more**:
diff --git a/docs/running-zingg/experience-zingg.md b/docs/running-zingg/experience-zingg.md
index 0fe9a97b0..367c0b62f 100644
--- a/docs/running-zingg/experience-zingg.md
+++ b/docs/running-zingg/experience-zingg.md
@@ -4,9 +4,9 @@ description: >-
   configuration, no data preparation needed.
 ---
 
-# Experience Zingg
+# 🚀 Experience Zingg
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 **Prerequisites:** Docker installed on your machine. Nothing else required.
 {% endhint %}
 
@@ -44,7 +44,7 @@ docker run -v /tmp:/tmp -it zingg/zingg bash
 ./scripts/zingg.sh --phase match --conf examples/febrl/config.json
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 ### **What just happened**
 
 Zingg read the `FEBRL` sample dataset bundled in the Docker image, applied a pre-trained entity resolution model to it, and wrote the resolved output to the output path configured in `examples/febrl/config.json`.
@@ -64,7 +64,7 @@ The output contains all your input fields plus three columns added by Zingg:
 * `Z_MAXSCORE` - the highest similarity score between any two records in that cluster.\
   Indicates the strongest match within the cluster.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: To know more about output scores, check [Interpret Output Score](../interpreting-results/interpret-output-scores.md).
 {% endhint %}
 
@@ -104,6 +104,6 @@ As long as your input columns and field types are not changing, the same model w
 
 </details>
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Enterprise adds persistent identity with Zingg ID, incremental matching, and production-grade notebooks. [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact)
 {% endhint %}
diff --git a/docs/running-zingg/generate-model-documentation.md b/docs/running-zingg/generate-model-documentation.md
index fc16de823..cde90a8dd 100644
--- a/docs/running-zingg/generate-model-documentation.md
+++ b/docs/running-zingg/generate-model-documentation.md
@@ -4,9 +4,9 @@ description: >-
   quality before committing to the train phase.
 ---
 
-# Generate Model Documentation
+# 📊 Generate Model Documentation
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Run this BETWEEN `label` and `train`. Do not skip - it lets you inspect training data quality before the model is built.
 {% endhint %}
 
@@ -36,7 +36,7 @@ zingg.initAndExecute()
 {% tab title="Enterprise" %}
 ### **Python**
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise uses `EZingg` instead of `Zingg`.
 {% endhint %}
 
@@ -65,7 +65,7 @@ zingg.initAndExecute()
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 The generated documentation is viewable in a browser. It shows all pairs you labelled as matches and non-matches in a readable format. Share this with subject matter experts to validate training data quality before running `train`.
 
 [Build and Save the Model ](build-and-save-the-model.md)→ run `train` once you are satisfied with the training data quality.
diff --git a/docs/running-zingg/install-zingg.md b/docs/running-zingg/install-zingg.md
index baa6435cc..eb8c516b3 100644
--- a/docs/running-zingg/install-zingg.md
+++ b/docs/running-zingg/install-zingg.md
@@ -4,7 +4,7 @@ description: >-
   Spark and notebook environments
 ---
 
-# Install Zingg
+# 🔌 Install Zingg
 
 Zingg runs on Spark (all editions) or Snowflake (Enterprise only). Select your platform from the tabs below. Each tab covers both Community and Enterprise where applicable.
 
@@ -112,7 +112,7 @@ pip show zingg
 1. In your workspace, click **New Item** and select **Lakehouse**. Name it and go inside it.
 2. Click **Get Data** and upload your data file as CSV or Parquet.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 Note the `abfss` path of your Lakehouse for use in the notebook. It follows this format: `abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/<lakehouse-id>/Files`
 
 For the full step-by-step Fabric guide including screenshots → [Run on Microsoft Fabric](../platform-guides/platform-guide-for-microsoft-fabric.md)
@@ -209,7 +209,7 @@ For production jobs, use a Glue Job with the Zingg JAR and Python packages.
    - `--additional-python-modules`: `zingg==0.7.0,tabulate,ipywidgets`
 4. Set **Worker type** to `G.1X` or `G.2X` and **Number of workers** based on data size.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For the full AWS Glue guide including IAM roles, S3 setup, and screenshots → [Platform Guide for AWS Glue](../platform-guides/platform-guide-for-aws-glue.md)
 {% endhint %}
 {% endtab %}
@@ -231,7 +231,7 @@ Install the Zingg Python package in JupyterLab:
 %pip install ipywidgets
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For the full GCP Dataproc guide including cluster creation, JAR upload, and screenshots → [Platform Guide for GCP Dataproc](../platform-guides/platform-guide-for-gcp-dataproc.md)
 {% endhint %}
 {% endtab %}
@@ -270,13 +270,13 @@ zinggDir = "abfss://container@storageaccount.dfs.core.windows.net/zingg/models"
 modelId = "synapseModel"
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For the full Azure Databricks guide which shares similar concepts → [Platform Guide for Azure Databricks](../platform-guides/platform-guide-for-azure-databricks.md)
 {% endhint %}
 {% endtab %}
 
 {% tab title="Local Spark" %}
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="rocket" %}
 Docker is the fastest way to get started locally. Use installing from release if you need a specific Spark version or want to integrate with an existing Spark installation.
 {% endhint %}
 
@@ -345,7 +345,7 @@ This builds Zingg models and finds duplicates in `examples/febrl/test.csv`. You
 {% endtab %}
 
 {% tab title="Enterprise Snowflake" %}
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="cloud" %}
 Enterprise only. Zingg on Snowflake uses the Enterprise Snowflake package and runs natively inside Snowflake using Snowpark — no Spark cluster required.
 {% endhint %}
 
@@ -388,7 +388,7 @@ mv ~/zingg.license .
 ```
 {% endcode %}
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Add `ZINGG_SNOW_JAR` and `ZINGG_SNOW_HOME` to `.bashrc` so they persist across sessions. 
 
 For the full end-to-end Snowflake guide → [Run on Snowflake](../platform-guides/platform-guide-for-snowflake.md)
diff --git a/docs/running-zingg/knowledge-graph.md b/docs/running-zingg/knowledge-graph.md
index 3d913e116..12cc39392 100644
--- a/docs/running-zingg/knowledge-graph.md
+++ b/docs/running-zingg/knowledge-graph.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Knowledge Graph
+# 🔗 Knowledge Graph
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise Plus only. Knowledge Graph is available in Zingg Enterprise Plus (ZinggES). Not available in Community, Enterprise Lite, or Enterprise.
 {% endhint %}
 
@@ -39,7 +39,7 @@ _**CHECK WITH TEAM/SONAL—Knowledge Graph is confirmed as an Enterprise Plus fe
 
 _**Full content and code example will be added once the above is confirmed**_
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:**
 
 * Entity resolution and graph analysis with Neo4j - [Connect Graph Databases](../connect-your-data/connect-graph-databases-neo4j.md)
diff --git a/docs/running-zingg/link-across-datasets.md b/docs/running-zingg/link-across-datasets.md
index 072b4ed14..1e409b13b 100644
--- a/docs/running-zingg/link-across-datasets.md
+++ b/docs/running-zingg/link-across-datasets.md
@@ -4,9 +4,9 @@ description: >-
   trained Zingg model.
 ---
 
-# Link across Datasets
+# 🔗 Link across Datasets
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `Link` and `Match` are two EQUAL operations. Both use the same trained model. Neither is a subset of the other.
 
 * `Link` - match records across two datasets. Each record from the first source is matched with all records from the remaining sources.
@@ -42,7 +42,7 @@ zingg.initAndExecute()
 ./scripts/zingg.sh --phase link --conf config.json
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 The link config file needs two data sources defined. Sample config for link available at:\
 `github.com/zinggAI/zingg/blob/main/examples/febrl/configLink.json`
 {% endhint %}
@@ -84,7 +84,7 @@ output = spark.read.csv(
 display(output)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise link output includes Zingg ID instead of `Z_CLUSTER`, plus the `Z_SOURCE` column identifying the source dataset of each record.
 {% endhint %}
 {% endtab %}
@@ -98,7 +98,7 @@ Enterprise link output includes Zingg ID instead of `Z_CLUSTER`, plus the `Z_SOU
 --properties-file <location to snowflake.properties>
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise link output includes Zingg ID instead of `Z_CLUSTER`, plus the `Z_SOURCE` column identifying the source dataset of each record.
 {% endhint %}
 
diff --git a/docs/running-zingg/lookup-data.md b/docs/running-zingg/lookup-data.md
index 9e03cd7b4..371459d02 100644
--- a/docs/running-zingg/lookup-data.md
+++ b/docs/running-zingg/lookup-data.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Lookup Data
+# 🔗 Lookup Data
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 **Enterprise** only. The lookup feature assigns a Zingg ID to given lookup records by finding which entity cluster they belong to in the existing match output.
 {% endhint %}
 
diff --git a/docs/running-zingg/pass-through.md b/docs/running-zingg/pass-through.md
index c331ddc84..14cb2e758 100644
--- a/docs/running-zingg/pass-through.md
+++ b/docs/running-zingg/pass-through.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Pass Through
+# ⚙️ Pass Through
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise only. Pass Through excludes specific records from the matching process while still including them in the output. Each Pass Through record receives its own unique Zingg ID.
 {% endhint %}
 
@@ -108,7 +108,7 @@ Pass-through records are written to the same output table as matched records, ea
 * Normally matched records share a `Zingg ID` with other records in their cluster.
 * `Pass Through` records each receiving their own unique `Zingg ID`. They are not grouped with any other record. They are traceable and auditable but do not contribute to or inherit any entity cluster.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Pass Through concept in the glossary - [Concepts glossary](../frequently-asked-questions/concept-glossary.md)
diff --git a/docs/running-zingg/quick-start-docker.md b/docs/running-zingg/quick-start-docker.md
index 6defe45d1..c2fa8baf8 100644
--- a/docs/running-zingg/quick-start-docker.md
+++ b/docs/running-zingg/quick-start-docker.md
@@ -5,17 +5,25 @@ description: >-
   Community (open source) and Enterprise
 ---
 
-# Quick Start (Docker)
+# 🚀 Quick Start (Docker)
 
 This page walks you through the full Zingg workflow on your local machine using Docker. You will install Zingg, connect sample data, find candidate pairs, label those pairs, train the model, and run match to see your first results. By the end you will have run every phase of the Zingg workflow and seen entity resolution working on your local machine with real data.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="info" icon="video" %}
+**See the full workflow in action:**
+{% endhint %}
+
+{% embed url="https://www.youtube.com/watch?v=F5Dw1QH0idQ" %}
+Entity Resolution with Open Source Zingg — end-to-end workflow demo.
+{% endembed %}
+
+{% hint style="success" icon="book-open" %}
 New to entity resolution? Read [Entity Resolution](../entity-resolution/) for the problem space and why Zingg's approach works.
 {% endhint %}
 
 {% tabs %}
 {% tab title="Local Spark (Docker)" %}
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="rocket" %}
 Fastest way to get started. Uses Docker and the Zingg Python API. No Spark cluster setup required. Covers Community (open source) and Enterprise - pull the Docker image and follow the same steps for either edition.
 {% endhint %}
 
@@ -62,7 +70,7 @@ docker cp /path/to/your-data.csv <container_id>:/zingg/your-data.csv
 ```
 {% endcode %}
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 Replace `<container_id>` with your running container's ID (find it with `docker ps`).
 {% endhint %}
 
@@ -70,7 +78,7 @@ Replace `<container_id>` with your running container's ID (find it with `docker
 
 Create or edit your configuration. The config defines your field definitions, input and output paths, model ID, and partition settings. Both editions use the same JSON structure — only the Python class differs between editions.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `FUZZY` handles typos and abbreviations. `EXACT` requires character-for-character match. `DONT_USE` excludes a field from matching but keeps it in output. For all match types → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
 {% endhint %}
 
@@ -159,7 +167,7 @@ outputPipe.setHeader("true")
 args.setOutput(outputPipe)
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 The JSON config blocks below are the equivalent declarations of the Python above. Use either approach - Python API for in-notebook orchestration, JSON for shell-driven workflows. Community and Enterprise use the same JSON structure with the addition of `outputStats` in Enterprise.
 {% endhint %}
 
@@ -276,7 +284,7 @@ The JSON config blocks below are the equivalent declarations of the Python above
 }
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 `outputStats` is Enterprise-only. The `$ZINGG_DYNAMIC_STAT_NAME` placeholder is replaced at runtime with `SUMMARY`, `CLUSTER`, or `RECORD` for the three different stats files Zingg produces.
 
 For full configuration schema with all parameters → [Configuration Schema](../reference/configuration-schema.md)
@@ -318,7 +326,7 @@ Zingg shows you the pairs selected by `findTrainingData`. For each pair, decide:
 * `0` - Not a match: these records are different entities
 * `2` - Not sure: when you cannot decide
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Zingg selects the most informative pairs from your data - not random samples. Label until all field types and data variation patterns in your schema are represented. Repeat Steps 4–5 in a loop if needed. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -410,7 +418,7 @@ For threshold guidance and full output column definitions → [Interpret Output
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Completed the walkthrough? Next steps:
 
 * Connect your own data - [Connect Data](../connect-your-data/pipes-and-data-connections.md)
@@ -418,6 +426,6 @@ Completed the walkthrough? Next steps:
 * Understanding output scores - [Interpreting output](../interpreting-results/interpret-output-scores.md)
 {% endhint %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Enterprise Quick Start uses EArguments, ECsvPipe, and EZingg in 7 production-grade notebooks. [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact) to get access to the full notebook sequence.
 {% endhint %}
diff --git a/docs/running-zingg/reassign-zingg-id.md b/docs/running-zingg/reassign-zingg-id.md
index bed26093c..7d8f46f39 100644
--- a/docs/running-zingg/reassign-zingg-id.md
+++ b/docs/running-zingg/reassign-zingg-id.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Reassign Zingg ID
+# 🔗 Reassign Zingg ID
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise Plus only. Preserves Zingg IDs when switching to a new model. Maximises ID continuity and minimises downstream disruption. Available in ZinggES (Enterprise Plus).
 {% endhint %}
 
@@ -18,7 +18,7 @@ When you evolve your Zingg setup - upgrading your model, migrating infrastructur
 
 The `reassignZinggId` phase carries Zingg IDs from your original production model to a new model's output. It maps clusters from the new model back to the original cluster assignments and preserves the original Zingg IDs wherever clusters overlap. New IDs are generated only where no match is found.
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 For what the Zingg ID is and how it works in Enterprise output → [Zingg ID and Z Cluster](../zingg-concepts/z-cluster-and-zingg-id.md)
 {% endhint %}
 
diff --git a/docs/running-zingg/run-incremental-matching.md b/docs/running-zingg/run-incremental-matching.md
index b48ec12e3..b8dfe65bb 100644
--- a/docs/running-zingg/run-incremental-matching.md
+++ b/docs/running-zingg/run-incremental-matching.md
@@ -8,13 +8,13 @@ tags:
     primary: true
 ---
 
-# Run Incremental Matching
+# 🚀 Run Incremental Matching
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 **Enterprise only.** Incremental matching updates the identity graph and preserves Zingg IDs across runs. Not available in Community.
 {% endhint %}
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 **BEFORE YOU BEGIN:** The initial match phase must have been completed first. Run incremental only after a full match has been run on your base dataset.
 
 Output is written to the same location as your match output. There is no separate output path for\
@@ -143,7 +143,7 @@ incrArgs = IncrementalArguments()
 incrArgs.setParentArgs(args)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 `setParentArgs()` inherits all base configuration from notebook 01 - field definitions, model ID, zinggDir, and output pipe. You only need to configure the incremental input pipe separately.
 {% endhint %}
 
@@ -176,7 +176,7 @@ output = spark.read.csv(
 display(output)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Output is at the same path as your match output. New records get new Zingg IDs. Records matching existing clusters get the existing Zingg ID. No separate output path is needed.
 {% endhint %}
 {% endtab %}
diff --git a/docs/running-zingg/run-the-match-phase.md b/docs/running-zingg/run-the-match-phase.md
index d51bf9f3f..e42080cae 100644
--- a/docs/running-zingg/run-the-match-phase.md
+++ b/docs/running-zingg/run-the-match-phase.md
@@ -4,13 +4,13 @@ description: >-
   your trained Zingg model.
 ---
 
-# Run the match phase
+# 🔗 Run the match phase
 
 The `match` phase runs AFTER `train`. It applies the trained Zingg model to your full dataset and groups records that represent the same real-world entity into clusters.
 
 Use `match` when you want to find duplicates within a single dataset. If you need to match records across two separate datasets, use the link phase instead - both are equal operations using the same trained model.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:**
 
 * Scores are explained in detail - [Interpreting output scores](../interpreting-results/interpret-output-scores.md)
@@ -40,7 +40,7 @@ output = spark.read.csv("path-to-output-directory",header = True)
 display(output)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Matching records share the same `Z_CLUSTER` value.
 {% endhint %}
 {% endtab %}
@@ -68,7 +68,7 @@ output = spark.read.csv("path-to-output-directory",header=True)
 display(output)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise output includes `Zingg ID` (persistent across runs) instead of `Z_CLUSTER`, plus deterministic match flag and Match Statistics. `Zingg ID` is stable across all subsequent incremental runs.
 {% endhint %}
 {% endtab %}
@@ -123,7 +123,7 @@ Once the job completes, stop the compute pool to release resources.
 ALTER COMPUTE POOL CONTAINER_ZINGG_POOL STOP ALL;
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise output includes `Zingg ID` (persistent across runs) instead of `Z_CLUSTER`, plus deterministic match flag and Match Statistics. `Zingg ID` is stable across all subsequent incremental runs.
 {% endhint %}
 {% endtab %}
diff --git a/docs/running-zingg/set-deterministic-matching-conditions.md b/docs/running-zingg/set-deterministic-matching-conditions.md
index fcbc69a5a..9b9ddce5f 100644
--- a/docs/running-zingg/set-deterministic-matching-conditions.md
+++ b/docs/running-zingg/set-deterministic-matching-conditions.md
@@ -5,9 +5,9 @@ tags:
   - enterprise-only
 ---
 
-# Set Deterministic Matching Conditions
+# ⚙️ Set Deterministic Matching Conditions
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Deterministic matching is Enterprise only. Available in Enterprise Lite and above
 {% endhint %}
 
@@ -41,7 +41,7 @@ Do not use deterministic matching on fields that are frequently incomplete or in
 
 If you are unsure, start with probabilistic matching only. Add deterministic rules once you have reviewed match output and identified high-confidence identifier combinations in your data.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:** [Configure Zingg](configure-zingg.md) | [Concepts glossary](../frequently-asked-questions/concept-glossary.md)
 {% endhint %}
 
diff --git a/docs/running-zingg/step-by-step-guide.md b/docs/running-zingg/step-by-step-guide.md
index 18bed4d81..593e054c7 100644
--- a/docs/running-zingg/step-by-step-guide.md
+++ b/docs/running-zingg/step-by-step-guide.md
@@ -4,17 +4,17 @@ description: >-
   for the entire Zingg docs site.
 ---
 
-# Step-by-Step Overview
+# 🚀 Step-by-Step Overview
 
 Zingg works in phases. Each phase performs a specific task and produces an output that the next phase depends on. You do not need to run all phases every time. Once the model is trained, you run `match`, `link`, or `runIncremental` directly.
 
 The steps below represent the complete workflow from first setup to production matching. This page describes the workflow you follow once Zingg is installed.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 This page describes the workflow you follow once Zingg is installed.\
 For installing Zingg on your platform → [Install Zingg](install-zingg.md).
 
-New to entity resolution or want to understand the problem space before diving in? → E[ntity Resolution](../entity-resolution/)
+New to entity resolution or want to understand the problem space before diving in? → [Entity Resolution](../entity-resolution/)
 {% endhint %}
 
 {% stepper %}
@@ -25,8 +25,8 @@ New to entity resolution or want to understand the problem space before diving i
 
 <summary>Define your data and output</summary>
 
-{% hint style="success" icon="right-long" %}
-**Read more**: [Content your Data](../connect-your-data/pipes-and-data-connections.md)
+{% hint style="success" icon="plug" %}
+**Read more**: [Connect your Data](../connect-your-data/pipes-and-data-connections.md)
 {% endhint %}
 
 Configure input and output pipes to tell Zingg where your source data lives and where to write results. Zingg connects to the data where it lives - it does not move it.
@@ -37,8 +37,8 @@ Configure input and output pipes to tell Zingg where your source data lives and
 
 <summary>Configure fields</summary>
 
-{% hint style="success" icon="right-long" %}
-**Read more**: [Content your Data](../connect-your-data/pipes-and-data-connections.md)
+{% hint style="success" icon="plug" %}
+**Read more**: [Connect your Data](../connect-your-data/pipes-and-data-connections.md)
 {% endhint %}
 
 Define your field definitions, match types, relevant Enterprise features (deterministic matching, primary key, pass through, standardization). The configuration drives every downstream phase.
@@ -59,7 +59,7 @@ Define your field definitions, match types, relevant Enterprise features (determ
 
 Run `findTrainingData` to generate candidate pairs, then label each pair as Match, No Match, or Uncertain. Zingg selects the most informative pairs, not random samples. Repeat the find-and-label cycle until all field types and data variation patterns in your schema are represented in your labeled data.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * [Create Training Data](create-training-data/)
@@ -72,7 +72,7 @@ Run `findTrainingData` to generate candidate pairs, then label each pair as Matc
 
 Run `verifyBlocking` to check what percentage of your known matching pairs are being blocked together correctly. Run after labelling and before committing to a full training run. If coverage is low, return to labelling. 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Verify Blocking](create-training-data/verify-blocking.md)
 {% endhint %}
 {% endstep %}
@@ -82,7 +82,7 @@ Run `verifyBlocking` to check what percentage of your known matching pairs are b
 
 Run `generateDocs` to produce a human-readable HTML report of your training data, including pairs labeled as matches and non-matches. Useful for sharing with subject-matter experts for feedback before training. Run `updateLabel` if needed to incorporate SME comments. 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Generate Model Documentation](generate-model-documentation.md)
 {% endhint %}
 {% endstep %}
@@ -92,7 +92,7 @@ Run `generateDocs` to produce a human-readable HTML report of your training data
 
 Run `train` to build and save the blocking and similarity models from your labelled training data. Models are written to `zinggDir/modelId` and can be reused on new data without retraining.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Build and Save the Model](build-and-save-the-model.md)
 {% endhint %}
 {% endstep %}
@@ -107,7 +107,7 @@ Apply the trained model to your data:
 
 `match` and `link` are two equal operations using the same trained model. As long as your input columns and field types are not changing, you do not need to rebuild the model.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * [Run the Match Phase](run-the-match-phase.md)
@@ -122,7 +122,7 @@ Review output scores to understand match quality. Use `Z_MINSCORE` and `Z_MAXSCO
 
 Run `explain` to understand cluster formation and check the output statistics (Enterprise Only) to understand and explain matches. 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
 {% endhint %}
 {% endstep %}
@@ -134,18 +134,18 @@ If match results need improvement, return to find-and-label with focused trainin
 
 If the Zingg job is slow, add more labels. The blocking model learns from the labels and adding more matches will help it branch better. 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Improve Accuracy](../tuning/improve-accuracy/)
 {% endhint %}
 {% endstep %}
 {% endstepper %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Enterprise: 7 production-grade notebooks, one per phase, with step-isolated execution and\
 full Python API support. [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact)
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Understanding the concepts behind each phase - [Overview section](../) | [Concepts glossary](../frequently-asked-questions/concept-glossary.md)
diff --git a/docs/running-zingg/update-label.md b/docs/running-zingg/update-label.md
index 717dbaeae..818180575 100644
--- a/docs/running-zingg/update-label.md
+++ b/docs/running-zingg/update-label.md
@@ -4,9 +4,9 @@ description: >-
   re-labeling everything from scratch.
 ---
 
-# Update Label
+# 🧪 Update Label
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="triangle-exclamation" %}
 Keep a backup of your model folder before running `updateLabel` - it overwrites previously saved labels.
 {% endhint %}
 
@@ -34,6 +34,6 @@ zingg.initAndExecute()
 
 The command opens an interactive prompt in the terminal. Enter the cluster ID of the pair you want to relabel, review the pair it displays, then enter the corrected decision: Match (1), No Match (2), or Can't Say (0). Enter 9 at either prompt to exit.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 After updating labels, run [Generate Model Documentation](generate-model-documentation.md) again to confirm the change, then re-run `train` once you're satisfied.
 {% endhint %}

From 6ed95b9a5e36e990783937a06a3ca5cb3387a4a6 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 13:41:49 +0530
Subject: [PATCH 6/9] docs: restyle connect-your-data and platform-guides
 sections for issue #1363

---
 .../connect-cloud-storage.md                  | 16 +++----
 .../connect-cloud-warehouses/README.md        |  4 +-
 .../connect-azure-databricks.md               | 10 ++--
 .../connect-bigquery.md                       |  4 +-
 .../connect-exasol.md                         |  4 +-
 .../connect-microsoft-fabric.md               |  4 +-
 .../connect-redshift.md                       |  2 +-
 .../connect-snowflake.md                      |  8 ++--
 .../connect-your-data/connect-file-formats.md | 16 +++----
 .../connect-graph-databases-neo4j.md          |  4 +-
 .../connect-nosql-databases.md                | 12 ++---
 .../connect-relational-databases.md           |  6 +--
 .../pipes-and-data-connections.md             |  6 +--
 .../platform-guide-for-aws-emr.md             |  8 ++--
 .../platform-guide-for-aws-glue.md            | 28 +++++------
 .../platform-guide-for-azure-databricks.md    | 48 +++++++++++--------
 .../platform-guide-for-gcp-dataproc.md        | 22 ++++-----
 .../platform-guide-for-microsoft-fabric.md    | 28 +++++------
 .../platform-guide-for-snowflake.md           | 22 ++++-----
 19 files changed, 130 insertions(+), 122 deletions(-)

diff --git a/docs/connect-your-data/connect-cloud-storage.md b/docs/connect-your-data/connect-cloud-storage.md
index 30a70761c..1a4f59de6 100644
--- a/docs/connect-your-data/connect-cloud-storage.md
+++ b/docs/connect-your-data/connect-cloud-storage.md
@@ -4,9 +4,9 @@ description: >-
   S3, Azure Blob Storage, and Google Cloud Storage.
 ---
 
-# Connect Cloud Storage
+# ☁️ Connect Cloud Storage
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](pipes-and-data-connections.md).
 {% endhint %}
 
@@ -57,7 +57,7 @@ args.setOutput(outputPipe)
 
 ### Python API - Enterprise
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise uses `ECsvPipe`. Replace import and class name only - the `s3a://` path pattern stays the same.
 {% endhint %}
 
@@ -151,7 +151,7 @@ zingg28032023/zingg
 conf examples / febrl / config.json-- zinggDir s3a:  // zingg28032023/zingg
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 Setting `zinggDir` to an S3 path stores all Zingg model files and training data in S3. Models are saved at: `your-bucket/zingg/your-model-id/`
 {% endhint %}
 {% endtab %}
@@ -159,7 +159,7 @@ Setting `zinggDir` to an S3 path stores all Zingg model files and training data
 {% tab title="Azure Blob" %}
 On Azure Blob Storage (ADLS Gen2), data lives in containers accessed via the `abfss://` path format. Use `abfss://<container>@<storage-account>.dfs.core.windows.net/<path-to-file>` to connect Zingg to your blob storage. All formats — CSV, Parquet, JSON, Avro, Delta — are available in both Community and Enterprise.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full Zingg installation, Azure Databricks cluster setup, and any additional dependency configuration required when running Zingg on Azure, follow the [Azure Databricks Platform Guide](../platform-guides/platform-guide-for-azure-databricks.md) or [Azure Synapse/Fabric Platform Guide](../platform-guides/platform-guide-for-microsoft-fabric.md).
 {% endhint %}
 
@@ -196,7 +196,7 @@ args.setOutput(outputPipe)
 {% tab title="GCS" %}
 On Google Cloud Storage, data lives in GCS buckets accessed via the `gs://` path format. Use `gs://<bucket-name>/<path-to-file>` to connect Zingg to your bucket. All formats — CSV, Parquet, JSON, Avro, Delta — are available in both Community and Enterprise.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full Zingg installation, Dataproc cluster setup, and any additional dependency configuration required when running Zingg on Google Cloud, follow the [GCP Dataproc Platform Guide](../platform-guides/platform-guide-for-gcp-dataproc.md).
 {% endhint %}
 
@@ -330,13 +330,13 @@ GCS supports all standard Spark formats. Replace `format` and the path suffix in
 }
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 For Delta format on GCS, see the Azure Databricks tab — Delta is Enterprise only and works the same way with the path schema swapped to `gs://`.
 {% endhint %}
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * For cloud warehouses - [Connect Cloud Warehouses](connect-cloud-warehouses/)
diff --git a/docs/connect-your-data/connect-cloud-warehouses/README.md b/docs/connect-your-data/connect-cloud-warehouses/README.md
index d9bb51ef6..c84e07218 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/README.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/README.md
@@ -6,13 +6,13 @@ description: >-
 
 # Connect Cloud Warehouses
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Snowflake on this page means Snowflake as a DATA SOURCE, available in all editions. This is different from Snowflake as a COMPUTE ENGINE, which is Enterprise only and does not require Spark. For the Enterprise native Snowflake run, see [Install Zingg](../../running-zingg/install-zingg.md)
 {% endhint %}
 
 Zingg connects to cloud warehouses using the Zingg pipe abstraction.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](../pipes-and-data-connections.md)
 {% endhint %}
 
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-azure-databricks.md b/docs/connect-your-data/connect-cloud-warehouses/connect-azure-databricks.md
index e6fb57dea..455eed76d 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-azure-databricks.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-azure-databricks.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Connect Azure Databricks
+# 🔌 Connect Azure Databricks
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 All standard Spark formats (CSV, Parquet, JSON, Avro) are available in Community. Delta format and Unity Catalog are Enterprise only.
 {% endhint %}
 
@@ -20,7 +20,7 @@ On Azure Databricks, data sits in cloud storage (Azure Data Lake Storage Gen2) o
 
 ### **Python API - Community**
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 `dbfs:/` paths are deprecated in Databricks. Use Unity Catalog paths for tables or cloud storage paths (`s3a://`, `abfss://)` for files. For Unity Catalog table access, Enterprise uses UCPipe—see below.
 {% endhint %}
 
@@ -43,7 +43,7 @@ args.setOutput(outputPipe)
 
 ### Python API - Enterprise
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 **Enterprise** uses `ECsvPipe`. Supports all formats including Delta and Unity Catalog.
 {% endhint %}
 
@@ -76,7 +76,7 @@ inputPipe = UCPipe("databricksInput", "catalog.schema.tablename")
 args.setData(inputPipe)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise supports Unity Catalog via UCPipe. Replace catalog, schema, and tablename with your Unity Catalog path. UCPipe is recommended for governed Delta tables in Databricks Enterprise deployments.
 {% endhint %}
 
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-bigquery.md b/docs/connect-your-data/connect-cloud-warehouses/connect-bigquery.md
index e0251eea2..5fbc579cd 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-bigquery.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-bigquery.md
@@ -4,7 +4,7 @@ description: >-
   Requires two dependency JARs and Google Cloud credentials.
 ---
 
-# Connect BigQuery
+# ☁️ Connect BigQuery
 
 ### Prerequisites
 
@@ -73,7 +73,7 @@ args.setOutput(bqOutput)
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="plug" %}
 ### **JAR download links:**
 
 * **spark-bigquery-with-dependencies:** repo1.maven.org/maven2/com/google/\
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-exasol.md b/docs/connect-your-data/connect-cloud-warehouses/connect-exasol.md
index 6c00243a0..246745e68 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-exasol.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-exasol.md
@@ -4,7 +4,7 @@ description: >-
   editions.
 ---
 
-# Connect Exasol
+# ☁️ Connect Exasol
 
 ### Prerequisites
 
@@ -44,7 +44,7 @@ args.setOutput(exasolOutput)
 
 ### JSON Config <a href="#json-config-4" id="json-config-4"></a>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="plug" %}
 The host parameter must be the first internal node IPv4 address. Full Exasol connector options: `github.com/exasol/spark-connector/blob/main/doc/user_guide/user_guide.md`
 {% endhint %}
 
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-microsoft-fabric.md b/docs/connect-your-data/connect-cloud-warehouses/connect-microsoft-fabric.md
index dc8ccfee9..8396e5a80 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-microsoft-fabric.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-microsoft-fabric.md
@@ -4,9 +4,9 @@ description: >-
   Available in all editions.
 ---
 
-# Connect Microsoft Fabric
+# 🔌 Connect Microsoft Fabric
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 On Microsoft Fabric, data lives in OneLake. Use the `abfss://` path format to connect Zingg to your Lakehouse. Available in all editions.
 {% endhint %}
 
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md b/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
index f94c793b3..44f5d1168 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-redshift.md
@@ -3,6 +3,6 @@ description: >-
   Connect Zingg to Amazon Redshift via JDBC. Configuration details are being verified. Check back for JSON config, Python API, and supported formats.
 ---
 
-# Connect Redshift
+# ☁️ Connect Redshift
 
 **Redshift connector documentation is being confirmed. This page will be updated with the JSON config, Python API, and supported formats once the connection details are verified.**
diff --git a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
index 253c0795b..2b9e38abe 100644
--- a/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
+++ b/docs/connect-your-data/connect-cloud-warehouses/connect-snowflake.md
@@ -4,13 +4,13 @@ description: >-
   connector. Available in all editions.
 ---
 
-# Connect Snowflake
+# ☁️ Connect Snowflake
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 This `config` connects Zingg to Snowflake as a DATA SOURCE using Spark. Available in all editions.
 {% endhint %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="triangle-exclamation" %}
 Snowflake as a COMPUTE ENGINE (native Snowflake run without Spark) is Enterprise only.\
 [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact).
 
@@ -67,7 +67,7 @@ args.setData(snowflakePipe)
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 ### Supported file formats
 
 Snowflake as a data source connects to tables and views via the Snowflake Spark connector. File formats (CSV, Parquet) are handled by Snowflake internally before Zingg reads the data. No additional format config needed on this connector.
diff --git a/docs/connect-your-data/connect-file-formats.md b/docs/connect-your-data/connect-file-formats.md
index f2ad3cfa5..d55998804 100644
--- a/docs/connect-your-data/connect-file-formats.md
+++ b/docs/connect-your-data/connect-file-formats.md
@@ -4,9 +4,9 @@ description: >-
   formats.
 ---
 
-# Connect File Formats
+# ☁️ Connect File Formats
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](pipes-and-data-connections.md).
 {% endhint %}
 
@@ -17,7 +17,7 @@ For files stored in cloud platforms, use the format sections within each platfor
 * Files on Databricks or Fabric - Connect cloud warehouses
 * Files on S3 - Connect cloud storage
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 All file formats shown here are available in Community and Enterprise.
 
 The only edition difference is the pipe class in the Python API:
@@ -46,7 +46,7 @@ args.setOutput(outputPipe)
 
 ### Python API - Enterprise
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Enterprise uses ECsvPipe. The pipe setup is identical - only the class `name` and `import` changes.
 {% endhint %}
 
@@ -89,7 +89,7 @@ args.setOutput(outputPipe)
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 If your CSV has no header row, set `header`: `false` and define the schema field explicitly.
 
 Schema uses Spark SQL types: string, integer, double, date, timestamp.
@@ -110,7 +110,7 @@ args.setData(parquetPipe)
 
 ### **JSON Config**
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 Parquet uses `path` as the property key, not `location` as CSV uses.
 {% endhint %}
 
@@ -180,7 +180,7 @@ args.setData(jsonPipe)
 }
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 * For multiline JSON files, add `multiLine`: `true` to props.
 * For JSON Lines (one object per line), no additional props needed.
 {% endhint %}
@@ -243,7 +243,7 @@ xlsxPipe = CsvPipe("xlsxData", "path/to/file.xlsx", schema, format="com.crealyti
 args.setData(xlsxPipe)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For files on cloud platforms:
 
 * Databricks and Fabric - [Connect Cloud Warehouses](connect-cloud-warehouses/)
diff --git a/docs/connect-your-data/connect-graph-databases-neo4j.md b/docs/connect-your-data/connect-graph-databases-neo4j.md
index 2331b45d1..9d254cdd3 100644
--- a/docs/connect-your-data/connect-graph-databases-neo4j.md
+++ b/docs/connect-your-data/connect-graph-databases-neo4j.md
@@ -4,9 +4,9 @@ description: >-
   graph database.
 ---
 
-# Connect Graph Databases (Neo4j)
+# 🔗 Connect Graph Databases (Neo4j)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](pipes-and-data-connections.md).
 {% endhint %}
 
diff --git a/docs/connect-your-data/connect-nosql-databases.md b/docs/connect-your-data/connect-nosql-databases.md
index f898bc248..0277c4756 100644
--- a/docs/connect-your-data/connect-nosql-databases.md
+++ b/docs/connect-your-data/connect-nosql-databases.md
@@ -4,13 +4,13 @@ description: >-
   destinations.
 ---
 
-# Connect NoSQL Databases
+# ☁️ Connect NoSQL Databases
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](pipes-and-data-connections.md).
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 This page covers NoSQL database connections.
 
 * For relational databases see [Connect Relational Databases](connect-relational-databases.md).
@@ -21,7 +21,7 @@ All NoSQL connectors are available in Community and Enterprise.
 
 {% tabs %}
 {% tab title="Cassandra" %}
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 The config below shows Cassandra as a write destination (output).
 
 To read FROM Cassandra, use the same props under "`data`" with `args.setData()`.
@@ -87,12 +87,12 @@ args.setData(mongoPipe)
 }
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 * URI format: `mongodb://host:port/database.collection`
 * Authenticated: `mongodb://user:password@host:port/database.collection`
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * For relational databases - [Connect Relational Databases](connect-relational-databases.md)
diff --git a/docs/connect-your-data/connect-relational-databases.md b/docs/connect-your-data/connect-relational-databases.md
index 50061da2a..1885d0a3b 100644
--- a/docs/connect-your-data/connect-relational-databases.md
+++ b/docs/connect-your-data/connect-relational-databases.md
@@ -4,9 +4,9 @@ description: >-
   JDBC.
 ---
 
-# Connect Relational Databases
+# ☁️ Connect Relational Databases
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 New to Zingg pipes? Understand how pipes work before configuring them - [Pipes and data connections](pipes-and-data-connections.md).
 {% endhint %}
 
@@ -123,7 +123,7 @@ in `zingg.conf` before running.
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:**
 
 * For NoSQL databases - [Connect NoSQL databases](connect-nosql-databases.md)
diff --git a/docs/connect-your-data/pipes-and-data-connections.md b/docs/connect-your-data/pipes-and-data-connections.md
index 4e98bb8e9..d41e548a0 100644
--- a/docs/connect-your-data/pipes-and-data-connections.md
+++ b/docs/connect-your-data/pipes-and-data-connections.md
@@ -2,7 +2,7 @@
 description: What a Zingg pipe is and how you configure one to connect your data.
 ---
 
-# Pipes and Data Connections
+# ☁️ Pipes and Data Connections
 
 A Zingg pipe is a configuration object that tells Zingg where to find your source data and where to write resolved output. It is how Zingg connects to your datastore, whether that is a\
 CSV file, a Delta table, a Snowflake table, a Parquet path on S3, or any other Spark-supported connector.
@@ -145,7 +145,7 @@ Define pipes in the data and output sections of your JSON config file.
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 `CsvPipe` and the standard `Pipe` class are Community APIs. `ECsvPipe`, `UCPipe`, and the other `E*Pipe` classes are Enterprise APIs that add features like Unity Catalog table access, Lakehouse table support, and stats output. The connection properties and basic pipe operations are identical, only the class name and the import path change.
 
 See [Install Zingg → Snowflake](../running-zingg/install-zingg.md) for the full setup.
@@ -176,7 +176,7 @@ Zingg connects to any datastore that has a Spark connector, plus Snowflake nativ
 | Exasol | `com.exasol.spark` | All |
 | Redshift | `jdbc` (redshift driver) | All |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * For the full connection config including required JARs, props, and code examples for each datastore - [Cloud Warehouses](connect-cloud-warehouses/) | [Cloud Storage](connect-cloud-storage.md) | [File Formats](connect-file-formats.md) | [Relational Databases](connect-relational-databases.md) | [NoSQL Databases](connect-nosql-databases.md) | [Neo4j](connect-graph-databases-neo4j.md)
diff --git a/docs/platform-guides/platform-guide-for-aws-emr.md b/docs/platform-guides/platform-guide-for-aws-emr.md
index 074e7dfb9..7ad3c9cf9 100644
--- a/docs/platform-guides/platform-guide-for-aws-emr.md
+++ b/docs/platform-guides/platform-guide-for-aws-emr.md
@@ -4,13 +4,13 @@ description: >-
   resolution on AWS infrastructure.
 ---
 
-# Platform Guide for AWS EMR
+# 🔌 Platform Guide for AWS EMR
 
 AWS EMR provides managed Spark on AWS infrastructure. Zingg runs on EMR using the standard Python API for Community and EZingg for Enterprise. S3 is used for data storage and model persistence using the `s3a://` path format.
 
 {% tabs %}
 {% tab title="Community" %}
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Tested with EMR 7.3.0 (Spark 3.5.1). Recommended instance type: `m6g.xlarge` or larger for worker nodes.
 {% endhint %}
 
@@ -118,7 +118,7 @@ Check logs in `/mnt/var/log/spark/` or via the Spark UI at `http://<master-publi
 {% endtab %}
 
 {% tab title="Enterprise" %}
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise requires a Zingg licence and the Enterprise EMR package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -224,7 +224,7 @@ Use `explain` phase output for detailed match analysis.
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Tune accuracy → [Improve Accuracy](../tuning/improve-accuracy/)
diff --git a/docs/platform-guides/platform-guide-for-aws-glue.md b/docs/platform-guides/platform-guide-for-aws-glue.md
index e62577c6b..1f4ae6767 100644
--- a/docs/platform-guides/platform-guide-for-aws-glue.md
+++ b/docs/platform-guides/platform-guide-for-aws-glue.md
@@ -5,11 +5,11 @@ description: >-
   Enterprise.
 ---
 
-# Platform Guide for AWS GLUE
+# 🔌 Platform Guide for AWS GLUE
 
 AWS Glue provides serverless Spark on AWS. There are no servers to manage; you define your session configuration, Glue provisions the workers, and you pay only for what you use. Zingg runs on Glue Interactive Sessions using the standard Python API. S3 is used for all data input, output, model storage, and checkpointing.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 * Tested with AWS Glue version 5.0, worker type G.1X, Spark 3.5.
 * AWS Glue Interactive Sessions restrict standard Jupyter widgets. Zingg's usual labeling widget does not render in Glue notebooks. This guide uses a CSV-based review workflow instead - candidate pairs are exported to S3 for offline labeling and read back into the session. This is covered in detail in Step 14.
 {% endhint %}
@@ -141,7 +141,7 @@ This is the most important step for Glue. The `%%configure` magic cell must be *
 
 Replace `your-bucket` with your actual S3 bucket name throughout.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 `%%configure` must be the first cell executed. Running any other cell before this, including imports, starts the Glue session without the JARs. If this happens, stop the session (`%stop_session`) and start a fresh notebook.
 {% endhint %}
 
@@ -188,7 +188,7 @@ Verify it is set:
 spark.sparkContext.getCheckpointDir()
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Because Glue workers are temporary, storing checkpoints in S3 ensures Zingg can recover from any worker interruption during long-running training or match phases. Without this, a worker restart causes the entire job to fail.
 {% endhint %}
 
@@ -272,7 +272,7 @@ def count_labeled_pairs(marked_pd):
     return n_positive, n_negative, n_total
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 `boto3` is the AWS SDK for Python. It is used here for low-level S3 operations - scanning folders, reading labeled files, and deleting training data, that Spark cannot handle directly. `cleanModel()` uses a paginator to ensure all files are found even when a folder contains more than 1,000 objects.
 {% endhint %}
 
@@ -330,7 +330,7 @@ args.setOutput(outputPipe)
 print("Input and output pipes configured.")
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Zingg also supports Parquet and JSON output on S3. To push results downstream to Amazon Redshift, use the Redshift connector.
 
 For all connector formats → [Connect Relational Databases](../connect-your-data/connect-relational-databases.md)
@@ -361,7 +361,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `FUZZY` handles variations like 'Jon' vs 'John' or 'St' vs 'Street'. `EXACT` requires a character-for-character match. `DONT_USE` excludes a field from matching but keeps it in output.
 
 **Read more**: For all match types → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
@@ -376,13 +376,13 @@ args.setNumPartitions(4)
 args.setLabelDataSampleSize(0.5)
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 For a 2-worker G.1X cluster (4 vCPUs each), 4–8 partitions is a good starting point. Set `numPartitions` to approximately 2–3× your total worker vCPU count. For 1M+ records, reduce `labelDataSampleSize` to 0.01–0.05 to prevent the sampling phase from exhausting worker memory.
 {% endhint %}
 
 ### Notebook 01 continued: Find training data and label pairs
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 AWS Glue Interactive Sessions restrict standard Jupyter widgets. Zingg's standard `ipywidgets` labeling interface does not render in Glue notebooks. Instead, this guide exports candidate pairs as a CSV to S3 for offline review. You label each pair by entering 0, 1, or 2 in a spreadsheet, upload the file back to S3, and run a sync cell to feed the labels back into Zingg. The process is covered in Steps 15 and 16.
 {% endhint %}
 
@@ -473,7 +473,7 @@ print(f"Review sheet exported for {n_pairs} pairs to: {export_path}")
 ```
 
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 How to label the review sheet:
 
 1. Go to **S3 → your-bucket → review/** in the AWS Console and download the `part-00000-*.csv` file.
@@ -484,7 +484,7 @@ How to label the review sheet:
 {% endhint %}
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 14–16 in a loop until you reach this target. Label until all field types and data variation patterns in your schema are covered. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -589,7 +589,7 @@ except Exception as e:
     print(f"Documentation not found at expected S3 path. {e}")
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 Unlike other platforms, Glue cannot render HTML inline in the notebook. Download `model.html` from the S3 console and open it in a browser to view the documentation. Navigate to **S3 → your-bucket → models → modelId → docs** to find the file.
 
 `generateDocs` is optional. Skip it if you have 30–40 matches and 30–40 non-matches and are confident in your labeling quality.
@@ -658,7 +658,7 @@ print(f"Redundancy Reduced by: "
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Tune accuracy → [Improve Accuracy](../tuning/improve-accuracy/)
@@ -666,7 +666,7 @@ print(f"Redundancy Reduced by: "
 * Set up incremental for production → [Run Incremental Matching](../running-zingg/run-incremental-matching.md)
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Download the notebooks used in this guide:
 
 * Community notebooks (NB01–04): `github.com/zinggAI/zingg/tree/main/examples/aws-glue`
diff --git a/docs/platform-guides/platform-guide-for-azure-databricks.md b/docs/platform-guides/platform-guide-for-azure-databricks.md
index 6cff12158..28ba7a8e7 100644
--- a/docs/platform-guides/platform-guide-for-azure-databricks.md
+++ b/docs/platform-guides/platform-guide-for-azure-databricks.md
@@ -4,15 +4,23 @@ description: >-
   your first match result. Covers Community (open source) and Enterprise
 ---
 
-# Platform Guide for Azure Databricks
+# 🔌 Platform Guide for Azure Databricks
 
 Databricks is a fully managed Spark environment that integrates seamlessly with Zingg. Both Community and Enterprise run on the same Databricks cluster. The difference is the libraries, class names, and the additional phases available in Enterprise.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="info" icon="video" %}
+**Watch**: Zingg at the Databricks Community Meetup — entity resolution in action on Databricks.
+{% endhint %}
+
+{% embed url="https://www.youtube.com/watch?v=6yIiPe-rcIg" %}
+Zingg at the Databricks Community Meetup — end-to-end entity resolution on Databricks.
+{% endembed %}
+
+{% hint style="success" icon="circle-info" %}
 Tested with Databricks Runtime 16.4 LTS (Spark 3.5.2, Scala 2.12). Newer LTS versions with Spark 3.5 are compatible.
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the Databricks connector config including Delta format, see [Connect Azure Databricks](../connect-your-data/connect-cloud-warehouses/connect-azure-databricks.md).
 {% endhint %}
 
@@ -106,7 +114,7 @@ args.setLabelDataSampleSize(0.5)
 spark.conf.set("spark.sql.adaptive.enabled", False)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 For 100k records start with `labelDataSampleSize` between 0.1 and 0.5. For 1M+ records use 0.01 to 0.05. If `findTrainingData` takes too long, reduce by approximately 10× and try again. Disabling Adaptive Query Execution gives Zingg more predictable Spark behavior.
 {% endhint %}
 
@@ -183,7 +191,7 @@ args.setFieldDefinition([
     ssn])
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `FUZZY` handles variations like 'Jon' vs 'John' or 'St' vs 'Street'. `EXACT` requires a character-for-character match. `DONT_USE` excludes a field from matching but keeps it in the output - use this for identifiers like record IDs. For all match types → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
 {% endhint %}
 
@@ -226,7 +234,7 @@ A widget displays each candidate pair side by side. For each pair, select:
 The widget code handles the display and state management. Run the cell to render it.
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 10–12 until you reach this target. Label until all field types and data variation patterns in your schema are covered. If results need improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -290,7 +298,7 @@ print(df.count())
 
 ![Databricks match output table with Z\_MINSCORE, Z\_MAXSCORE and Z\_CLUSTER columns; several rows share Z\_CLUSTER 3, meaning they resolved to the same entity.](../.gitbook/assets/databricks-match-output.png)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Records sharing the same `Z_CLUSTER` value have been resolved to the same real-world entity. `Z_MINSCORE` is the weakest match confidence within the cluster. `Z_MAXSCORE` is the strongest. For full output column definitions → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md).
 {% endhint %}
 
@@ -314,7 +322,7 @@ Uses `EArguments`, `EFieldDefinition`, `ECsvPipe`, `EZinggWithSpark`. Seven note
 
 Enterprise adds blocking model configuration, a primary key for incremental matching, stats output, deterministic matching rules, pass-through expressions, the `runIncremental` phase, and the `explain` phase.
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Enterprise requires a Zingg licence and the `zinggEC` and `zinggES` packages. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -338,7 +346,7 @@ Enterprise requires a Zingg licence and the `zinggEC` and `zinggES` packages. [C
 !pip show zinggES
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 All three must show as installed: `zingg` (Community base), `zinggEC` (Enterprise), `zinggES` (Enterprise Plus). If any show as not found, install the corresponding `.whl` file from the cluster Libraries tab using the wheels provided in your Enterprise package.
 {% endhint %}
 
@@ -402,7 +410,7 @@ args.setNumPartitions(32)
 spark.conf.set("spark.sql.adaptive.enabled", False)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 Set `numPartitions` to approximately 20–30× your worker vCPU count. For a 4-node cluster with 8 vCPUs each, 32 is a good starting point. `labelDataSampleSize` is set in Notebook 03—see that notebook for guidance.
 {% endhint %}
 
@@ -450,7 +458,7 @@ statsOutputPipe.addProperty("header", "true")
 args.setOutputStats(statsOutputPipe)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:** If `outputStats` is not configured, Zingg skips stats writing and the run proceeds normally. For stats field definitions → [Output Statistics](../interpreting-results/output-statistics.md).
 {% endhint %}
 
@@ -498,7 +506,7 @@ args.setDeterministicMatchingCondition(dm1, dm2)
 args.setPassthroughExpr("fname = 'matilda'")
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * For deterministic matching concepts → [Configure Zingg](../running-zingg/configure-zingg.md)
@@ -541,7 +549,7 @@ street.setStopWords(zingg_dir + "/" + model_id + "/stopWords/" + stopwordcolumn)
 print(args.getArgs())
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:** Skip this notebook on the first run. Return to it if match accuracy on text-heavy fields needs improvement. For the full stopwords guide → [Remove Stopwords](../tuning/improve-accuracy/remove-stopwords-optional.md)
 {% endhint %}
 
@@ -555,7 +563,7 @@ print(args.getArgs())
 args.setLabelDataSampleSize(0.1)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 For 100k records use 0.1–0.5. For 1M records use 0.01–0.05. If `findTrainingData` takes too long, reduce by approximately 10× and try again.
 {% endhint %}
 
@@ -588,7 +596,7 @@ else:
 The Enterprise widget shows one pair at a time with Prev and Next navigation. For each pair select `Match`, `No Match`, or `Uncertain`. Labels are saved directly to the `DataFrame` as you click.
 
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="check-circle" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 17–19 until all field types and data patterns are represented. If accuracy needs improvement after the first match run, return here and focus on patterns that are underrepresented.
 {% endhint %}
 
@@ -653,7 +661,7 @@ print(outputDF.count())
 ```
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Enterprise output includes `ZINGG_ID` — a globally unique, persistent identifier for each resolved entity. Unlike `Z_CLUSTER` in Community, `ZINGG_ID` does not change between runs including incremental runs.
 
 * For output column definitions → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
@@ -682,7 +690,7 @@ outputTmpPipe.addProperty("header", "true")
 incrArgs.setOutputTmp(outputTmpPipe)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 `setOutputTmp` specifies a temporary path where Zingg writes intermediate results before merging them into the main output. It must be different from your main output path.
 {% endhint %}
 
@@ -702,7 +710,7 @@ display(outputDF)
 print(outputDF.count())
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full incremental matching guide including cluster merge and reassignment behaviour → [Run Incremental Matching](../running-zingg/run-incremental-matching.md)
 {% endhint %}
 
@@ -751,7 +759,7 @@ print(outputDF.count())
 ```
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Each row in the output represents a matched record pair within the cluster — `pk1` and `pk2` are the primary keys of the two records, with their similarity score.
 
 For the full explain guide → [Explain a Specific Cluster](../interpreting-results/explain-a-specific-cluster.md)
@@ -759,7 +767,7 @@ For the full explain guide → [Explain a Specific Cluster](../interpreting-resu
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Download the notebooks used in this guide:
 
 * Community notebooks (NB01–04): `github.com/zinggAI/zingg/tree/main/examples/databricks`
diff --git a/docs/platform-guides/platform-guide-for-gcp-dataproc.md b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
index 9e6d0d95e..a8cb8097f 100644
--- a/docs/platform-guides/platform-guide-for-gcp-dataproc.md
+++ b/docs/platform-guides/platform-guide-for-gcp-dataproc.md
@@ -5,11 +5,11 @@ description: >-
   source) and Enterprise.
 ---
 
-# Platform Guide for GCP Dataproc
+# 🔌 Platform Guide for GCP Dataproc
 
 Combining Zingg with Google Cloud gives you elastic Spark scale via Dataproc, flexible storage via GCS, and a managed JupyterLab workspace via the Component Gateway. Spin up a cluster when you need it and shut it down when the job is done.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Tested with Dataproc image version 2.2-debian12 (Spark 3.5). The `n2-standard-4` machine type with 16GB RAM per node is the recommended minimum for Zingg's training phases.
 {% endhint %}
 
@@ -81,7 +81,7 @@ gs://$BUCKET/spark-3.5-bigquery-0.44.1.jar,\
 gs://$BUCKET/gcs-connector-hadoop3-latest.jar"
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 `numPartitions` should be set to approximately 20–30× your worker vCPU count. For a 2-worker `n2-standard-4` cluster (8 vCPUs each), start with 4–8.
 {% endhint %}
 
@@ -224,7 +224,7 @@ outputPipe = CsvPipe("resultOutput", output_path)
 args.setOutput(outputPipe)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Zingg supports CSV, Parquet, and JSON output on GCS. To push results directly to BigQuery after matching, use the BigQuery Spark connector.
 
 For connector config → [Connect BigQuery](../connect-your-data/connect-cloud-warehouses/connect-bigquery.md)
@@ -255,7 +255,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `FUZZY` handles variations like 'Jon' vs 'John' or 'St' vs 'Street'. `EXACT` requires a character-for-character match. `DONT_USE` excludes a field from matching but keeps it in output.
 
 For all match types → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
@@ -277,7 +277,7 @@ Reduce to 0.1 if `findTrainingData` is slow on large datasets
 args.setLabelDataSampleSize(0.5)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 For 100k records use `labelDataSampleSize` between 0.1 and 0.5. For 1M+ records use 0.01 to 0.05. If `findTrainingData` takes too long, reduce by approximately 10× and try again.
 {% endhint %}
 
@@ -354,7 +354,7 @@ ready_for_save = True
 ```
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 11–14 in a loop until you reach this target. Label until all field types and data variation patterns in your schema are covered. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are underrepresented.
 {% endhint %}
 
@@ -411,7 +411,7 @@ with open(DOCS_DIR + "data.html", 'r') as f:
 ```
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 `generateDocs` is optional. Skip it if you have 30–40 matches and 30–40 non-matches and are confident in your labeling quality.
 {% endhint %}
 
@@ -460,7 +460,7 @@ final_results.show(10)
 ```
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 * `z_cluster`— unique entity ID assigned by Zingg. All records sharing the same `z_cluster` represent the same real-world entity. Group by `z_cluster` to collapse duplicates into a golden record.
 * `z_score` — model confidence. Values closer to 1.0 indicate a stronger match.
 * `z_zid` — unique internal row identifier assigned during this run.
@@ -470,7 +470,7 @@ For threshold guidance and full output column definitions → [Interpret Output
 {% endtab %}
 
 {% tab title="Enterprise" %}
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise requires a Zingg licence and the Enterprise GCP package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -627,7 +627,7 @@ zingg.initAndExecute()
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Tune accuracy → [Improve Accuracy](../tuning/improve-accuracy/)
diff --git a/docs/platform-guides/platform-guide-for-microsoft-fabric.md b/docs/platform-guides/platform-guide-for-microsoft-fabric.md
index b4e6effd7..2e5390769 100644
--- a/docs/platform-guides/platform-guide-for-microsoft-fabric.md
+++ b/docs/platform-guides/platform-guide-for-microsoft-fabric.md
@@ -5,11 +5,11 @@ description: >-
   and Enterprise.
 ---
 
-# Platform Guide for Microsoft Fabric
+# 🔌 Platform Guide for Microsoft Fabric
 
 Microsoft Fabric paired with Zingg gives you entity resolution with built-in data governance via Microsoft Purview. Fabric's OneLake provides a single storage layer for all your data. Zingg runs on Fabric Spark pools using the same Python API as on other platforms.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 * Tested with Fabric Runtime 1.3 (Spark 3.5).
 * Download the sample Fabric notebook from `github.com/zinggAI/zingg/blob/main/examples/fabric/ExampleNotebook.ipynb` and upload it to your workspace to follow this guide.
 {% endhint %}
@@ -68,7 +68,7 @@ Zingg reads from and writes to OneLake. Create a Lakehouse to give Zingg a stora
 
     ![Lakehouse Get data menu with Upload files selected.](../.gitbook/assets/fabric-lakehouse-upload-files.png)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Sample data for testing: `github.com/zinggAI/zingg/blob/main/examples/febrl/test.csv`
 
 Zingg also supports Delta Lake tables, Parquet, and JSON - change the format in the input pipe configuration.
@@ -187,7 +187,7 @@ args.setNumPartitions(32)
 spark.conf.set("spark.sql.adaptive.enabled", False)
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 Set `numPartitions` to approximately 20–30× your worker vCPU count. `labelDataSampleSize` is set in Notebook 02 where the labeling loop runs — see Step 13.
 {% endhint %}
 
@@ -245,7 +245,7 @@ outputPipe = CsvPipe("resultOutput", output_path)
 args.setOutput(outputPipe)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: To use Delta tables instead of CSV, change the format to `delta` and point to your Lakehouse Tables directory. For all connector formats → [Connect Microsoft Fabric](../connect-your-data/connect-cloud-warehouses/connect-microsoft-fabric.md)
 {% endhint %}
 
@@ -274,7 +274,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `FUZZY` handles variations like 'Jon' vs 'John' or 'St' vs 'Street'. `EXACT` requires a character-for-character match. `DONT_USE` excludes a field from matching but keeps it in output — use this for record identifiers. For all match types → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
 {% endhint %}
 
@@ -291,7 +291,7 @@ args.setNumPartitions(4)
 args.setLabelDataSampleSize(0.4)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 For 100k records use `labelDataSampleSize` between 0.1 and 0.5. For 1M+ records use 0.01 to 0.05. If `findTrainingData` is slow, reduce by approximately 10× and try again.
 {% endhint %}
 
@@ -370,7 +370,7 @@ ready_for_save = True
 
 ![Zingg labeling widget in a Fabric notebook showing two candidate records side by side with Uncertain, Match and No Match buttons.](../.gitbook/assets/fabric-labeling-widget.png)
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Target 30–40 match pairs and 30–40 non-match pairs before training. Repeat Steps 15–18 in a loop until you reach this target. Label until all field types and data variation patterns in your schema are covered. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -409,7 +409,7 @@ else:
     ready_for_save = False
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 `notebookutils.fs.mkdirs(MARKED_DIR)` creates the target directory in OneLake if it does not already exist. This is a Fabric-specific utility — it is not used on other platforms.
 {% endhint %}
 
@@ -480,7 +480,7 @@ print(outputDF.count())
 {% endtab %}
 
 {% tab title="Enterprise" %}
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise requires a Zingg licence and the Enterprise Fabric package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -551,7 +551,7 @@ Verify installation:
 !pip show zinggES
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 All three must show as installed: `zingg` (Community base), `zinggEC` (Enterprise), `zinggES` (Enterprise Plus). If any show as not found, install the corresponding `.whl` file from the cluster Libraries tab using the wheels provided in your Enterprise package.
 {% endhint %}
 
@@ -607,7 +607,7 @@ args.setNumPartitions(32)
 spark.conf.set("spark.sql.adaptive.enabled", False)
 ```
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="triangle-exclamation" %}
 Set `numPartitions` to approximately 20–30× your worker vCPU count. `labelDataSampleSize` is set in Notebook 03 where the labeling loop runs.
 {% endhint %}
 
@@ -805,14 +805,14 @@ zingg.initAndExecute()
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Tune accuracy → [Improve Accuracy](../tuning/improve-accuracy/)
 * Understand scores and set thresholds → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Download the notebooks used in this guide:
 
 * Community notebooks (NB01–04): `github.com/zinggAI/zingg/tree/main/examples/fabric`
diff --git a/docs/platform-guides/platform-guide-for-snowflake.md b/docs/platform-guides/platform-guide-for-snowflake.md
index 227529b68..d825b81ee 100644
--- a/docs/platform-guides/platform-guide-for-snowflake.md
+++ b/docs/platform-guides/platform-guide-for-snowflake.md
@@ -7,15 +7,15 @@ tags:
   - ent
 ---
 
-# Platform Guide for Snowflake
+# 🔌 Platform Guide for Snowflake
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="cloud" %}
 Run Zingg with Snowflake as your data source using the Snowflake Spark connector on EC2, or run Zingg natively inside Snowflake with Enterprise.
 {% endhint %}
 
 {% tabs %}
 {% tab title="Community" %}
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For the Snowflake connector JSON config → [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md) Sample Snowflake config file: `github.com/zinggAI/zingg/blob/main/examples/febrl/configSnow.json`
 {% endhint %}
 
@@ -51,7 +51,7 @@ Before connecting to Snowflake, download the Snowflake Spark connector JAR and t
 spark.jars=snowflake-jdbc-3.13.19.jar,spark-snowflake_2.12-2.10.0-spark_3.1.jar
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For JAR download links and full `zingg.conf` setup → [Connect Snowflake](../connect-your-data/connect-cloud-warehouses/connect-snowflake.md)
 {% endhint %}
 
@@ -110,7 +110,7 @@ Configure your `config.json` with your Snowflake connection details, field defin
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Replace `your-customer-table` with your source Snowflake table name and `your-output-table` with the table Zingg will create for resolved output. For all `fieldDefinition` parameters → [Configuration Schema](../reference/configuration-schema.md)
 
 Set `numPartitions` to approximately 20–30× your Snowflake warehouse vCPU count. For a standard XS warehouse (1 node), start with 4. For a LARGE warehouse (4 nodes), use 16–32. Reduce `labelDataSampleSize` to 0.05–0.1 for tables with more than 1M rows.
@@ -173,7 +173,7 @@ def lambda_handler(event, context):
         return {"statusCode" : 400, "body" : json.dumps({"data" : [[0, str(e)]]})}
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 The `checklog` option lets you read the Zingg log file directly from Snowflake to monitor phase progress without logging back into EC2.
 {% endhint %}
 
@@ -194,7 +194,7 @@ zip -g deploy.zip lambda_function.py
 zip -g deploy.zip your-key.pem
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 When the `.pem` key file is included in the zip, Lambda extracts it to `/var/task/your-key.pem` at runtime. The `key_filename` value in `lambda_function.py` must be set to `/var/task/your-key.pem` before zipping and deploying. If the path is wrong, the SSH connection will fail.
 {% endhint %}
 
@@ -206,7 +206,7 @@ When the `.pem` key file is included in the zip, Lambda extracts it to `/var/tas
 { "body" : "{\"data\": [[0, \"findTrainingData\"]]}" }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 A successful test returns `HTTP 200` and `Started phase: findTrainingData`. Monitor progress via `tail -f logfile.txt` on EC2.
 {% endhint %}
 
@@ -262,7 +262,7 @@ SELECT run_zingg('match');
 SELECT run_zingg('checklog');
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="check-circle" %}
 Run `findTrainingData` to generate candidate pairs. Zingg selects the most informative pairs from your data, not random samples. Run `label` interactively on EC2 to label those pairs as Match, No Match, or Uncertain. Label until all field types and data variation patterns in your schema are represented. If accuracy needs improvement after the first match run, return to labeling and focus on patterns that are missing or underrepresented.
 {% endhint %}
 
@@ -286,7 +286,7 @@ Or from Snowflake:
 SELECT run_zingg('checklog');
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Output is written to the Snowflake table configured in the `output` section of your `config.json`. Records with the same `Z_CLUSTER` value represent the same real-world entity. For output column definitions → [Interpret Output Scores](../interpreting-results/interpret-output-scores.md)
 {% endhint %}
 
@@ -337,7 +337,7 @@ Same as the main guide above; pull the Zingg Docker image, configure Lambda, and
 {% endtab %}
 
 {% tab title="Enterprise" %}
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise only. Zingg Enterprise runs natively inside Snowflake using Snowpark. No EC2, no Docker, and no Lambda required.
 
 Enterprise requires a Zingg licence and the Enterprise Snowflake package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).

From 8cf893c3eb7e4cfb16bec264fea6b3205a4737f3 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 14:36:43 +0530
Subject: [PATCH 7/9] docs: restyle P4 concepts, tuning, interpreting-results
 for issue #1363

---
 docs/README.md                                | 12 +++++++++-
 docs/entity-resolution/README.md              | 14 +++++++----
 .../rule-based-entity-resolution.md           | 16 ++++++-------
 .../explain-a-specific-cluster.md             |  4 ++--
 .../explainability-and-statistics.md          |  2 +-
 .../interpret-output-scores.md                | 16 ++++++-------
 .../interpreting-results/output-statistics.md |  6 ++---
 docs/tuning/blocking-strategy.md              |  2 +-
 .../tuning/configure-field-standardization.md |  2 +-
 docs/tuning/improve-accuracy/README.md        |  2 +-
 .../remove-stopwords-optional.md              |  2 +-
 docs/zingg-concepts/identity-graph.md         |  8 +++----
 docs/zingg-concepts/z-cluster-and-zingg-id.md |  6 ++---
 .../field-definition/match-types/README.md    |  2 +-
 .../match-types/dont_use-match.md             |  4 ++--
 .../match-types/email-match.md                |  2 +-
 .../match-types/email_optimised-match.md      |  4 ++--
 .../match-types/exact-match.md                |  2 +-
 .../match-types/fuzzy-match.md                |  2 +-
 .../match-types/fuzzy_optimised-match.md      |  4 ++--
 .../match-types/mapping_-filename-match.md    |  4 ++--
 .../match-types/null_or_blank-match.md        |  2 +-
 .../match-types/numeric-match.md              |  2 +-
 .../match-types/numeric_with_units-match.md   |  2 +-
 .../match-types/only_alphabets_exact-match.md |  2 +-
 .../match-types/only_alphabets_fuzzy-match.md |  2 +-
 .../only_alphabets_fuzzy_optimised-match.md   |  4 ++--
 .../match-types/pincode-match.md              |  2 +-
 .../match-types/text-match.md                 |  2 +-
 docs/zingg-concepts/zingg-phases.md           | 24 +++++++++----------
 30 files changed, 86 insertions(+), 72 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index f23dd8737..6b8c9f1a8 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -4,7 +4,9 @@ description: >-
   at any scale.
 ---
 
-# What is Zingg
+# 🧬 What is Zingg
+
+<mark style="color:orange;background-color:transparent;">**ML-powered entity resolution and record matching — built for your warehouse, at any scale.**</mark>
 
 Most teams don't have a duplication problem; they have an entity resolution problem. The same entity lives in five systems under three different names. Every downstream decision built on that data becomes unreliable: analytics produce conflicting numbers, compliance checks miss connections, and operational teams work from different versions of the same record. When AI agents are in the loop, the problem compounds; an agent doesn't know a record is a duplicate, so it acts on each one independently, multiplying errors at the speed of automation.
 
@@ -14,6 +16,14 @@ Zingg solves this with ML-powered entity resolution that runs directly on your i
 
 Whether you're resolving customers, patients, suppliers, citizens, or product issues - Zingg works on any entity type.
 
+{% hint style="info" icon="video" %}
+**See it in action:**
+{% endhint %}
+
+{% embed url="https://www.youtube.com/watch?v=zOabyZxN9b0" %}
+Zingg entity resolution demo — watch the end-to-end pipeline in action.
+{% endembed %}
+
 ### What do you want to do today?
 
 <table data-view="cards"><thead><tr><th></th><th data-type="content-ref"></th><th><select></select></th><th></th></tr></thead><tbody><tr><td><mark style="color:violet;background-color:violet;"><strong>What is entity resolution</strong></mark></td><td><a href="entity-resolution/">entity-resolution</a></td><td></td><td>Understand the problem Zingg solves, why fragmented entity data breaks downstream systems, and why rule-based approaches fail at scale.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Run Zingg for the first time</strong></mark></td><td><a href="running-zingg/step-by-step-guide.md">step-by-step-guide.md</a></td><td></td><td>Get Zingg running on your platform and see your first match result in under 30 minutes.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Connect your data</strong></mark></td><td><a href="connect-your-data/pipes-and-data-connections.md">pipes-and-data-connections.md</a></td><td></td><td>Configure Zingg pipes for your data source - cloud warehouses, cloud storage, databases, and file formats.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Build and train a model</strong></mark></td><td><a href="running-zingg/build-and-save-the-model.md">build-and-save-the-model.md</a></td><td></td><td>Configure your data, label training pairs, and build a model for your dataset.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>Interpret and tune results</strong></mark></td><td><a href="tuning/configure-field-standardization.md">configure-field-standardization.md</a></td><td></td><td>Understand your match output, set thresholds, and improve accuracy.</td></tr><tr><td><mark style="color:violet;background-color:violet;"><strong>References</strong></mark></td><td><a href="reference/configuration-schema.md">configuration-schema.md</a></td><td></td><td>Configuration schema, CLI commands, runtime properties, and FAQ.</td></tr></tbody></table>
diff --git a/docs/entity-resolution/README.md b/docs/entity-resolution/README.md
index 8f1ec0217..138f58c0d 100644
--- a/docs/entity-resolution/README.md
+++ b/docs/entity-resolution/README.md
@@ -4,10 +4,14 @@ description: >-
   Zingg handles each part of it systematically.
 ---
 
-# Entity Resolution
+# 📘 Entity Resolution
 
 Entity resolution is the process of identifying that multiple records across different systems represent the same real-world entity.
 
+{% embed url="https://www.youtube.com/watch?v=uMAsmly8kqE" %}
+A short explainer on entity resolution vs identity resolution.
+{% endembed %}
+
 It answers one question: Given fragmented, inconsistent data spread across sales,\
 marketing, support, and operations systems, whose records actually belong to the same\
 customer, supplier, patient, or product?
@@ -83,7 +87,7 @@ together, and the LLM reasons over a complete, entity-aware view.
 
 This process is the Identity RAG pattern entity resolution as infrastructure for AI accuracy.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Enhancing LLM applications with Zingg and LangChain](../recipes-and-integration/identity-rag-with-zingg-and-langchain.md)
 {% endhint %}
 
@@ -140,19 +144,19 @@ Not if your data has fuzzy variation. Graph databases like Neo4j are excellent a
 
 Zingg and graph databases work best together. Zingg does entity resolution, which means it finds out which records are for the same entity using ML-based probabilistic and deterministic matching. The graph database is used for downstream relationship analysis and inference in AML, KYC, Knowledge Graph, and Customer 360 scenarios.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="plug" %}
 **Read more**: [Connect graph databases](../connect-your-data/connect-graph-databases-neo4j.md)
 {% endhint %}
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="rocket" %}
 Ready to see entity resolution in action?
 
 * [Run Zingg on your local machine](../running-zingg/quick-start-docker.md) - full workflow in 30 minutes
 {% endhint %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Need entity resolution at production scale with persistent identity and incremental\
 processing?
 
diff --git a/docs/entity-resolution/rule-based-entity-resolution.md b/docs/entity-resolution/rule-based-entity-resolution.md
index 5d1b38684..27a0dcfb1 100644
--- a/docs/entity-resolution/rule-based-entity-resolution.md
+++ b/docs/entity-resolution/rule-based-entity-resolution.md
@@ -2,16 +2,16 @@
 description: Why custom matching logic breaks down
 ---
 
-# Rule based entity resolution
+# ⚙️ Rule based entity resolution
 
 Rule-based entity resolution fails for three reasons:
 
-1. **Rules cannot enumerate variation**. The number of ways a name, address, or company identifier can vary is unbounded. For every rule you write, new variations appear in production data that the rule does not cover. Maintaining a ruleset for a live dataset is a permanent, open-ended engineering commitment.
-2. **Rules cannot score confidence**. A rule fires, or it does not. It cannot tell you that two records are probably the same entity or that a cluster has a weak link worth human review. Entity resolution at production scale requires a graded confidence signal; not a binary match/no-match.
-3. **Rules do not scale to the comparison space**. At one million records, the naive approach requires evaluating 500 billion record pairs. At ten million, it is 50 trillion. A rule engine applied to every pair is computationally impossible.
+1. **Rules cannot enumerate variation.** The number of ways a name, address, or company identifier can vary is unbounded. For every rule you write, new variations appear in production data that the rule does not cover. Maintaining a ruleset for a live dataset is a *permanent, open-ended engineering commitment*.
+2. **Rules cannot score confidence.** A rule fires, or it does not. It cannot tell you that two records are probably the same entity or that a cluster has a weak link worth human review. Entity resolution at production scale requires a **graded confidence signal**, not a binary match/no-match.
+3. **Rules do not scale to the comparison space.** At one million records, the naive approach requires evaluating 500 billion record pairs. At ten million, it is 50 trillion. A rule engine applied to every pair is computationally impossible.
 
-Zingg's ML models solve all three:
+**Zingg's ML models solve all three:**
 
-* Zingg learns variation from your data - roughly 30 - 50 labeled examples are enough to build a model that generalizes to patterns it has not seen before.
-* It produces a graded confidence score (`Z_MINSCORE` and `Z_MAXSCORE`) per cluster, so you can route high-confidence matches to automated processing and low-confidence matches to human review.
-* It learns a blocking model to cut down the comparison space from billions of pairs to a tiny fraction without losing recall, so the similarity model only checks candidate pairs that could plausibly match.
+* Zingg learns variation from your data — roughly 30 - 50 labeled examples are enough to build a model that generalizes to patterns it has not seen before.
+* It produces a **graded confidence score** (`Z_MINSCORE` and `Z_MAXSCORE`) per cluster, so you can route high-confidence matches to automated processing and low-confidence matches to human review.
+* It learns a *blocking model* to cut down the comparison space from billions of pairs to a tiny fraction without losing recall, so the similarity model only checks candidate pairs that could plausibly match.
diff --git a/docs/interpreting-results/explain-a-specific-cluster.md b/docs/interpreting-results/explain-a-specific-cluster.md
index 0681bdefd..3f65b581d 100644
--- a/docs/interpreting-results/explain-a-specific-cluster.md
+++ b/docs/interpreting-results/explain-a-specific-cluster.md
@@ -8,7 +8,7 @@ tags:
     primary: true
 ---
 
-# Explain a Specific Cluster
+# 📊 Explain a Specific Cluster
 
 {% hint style="info" icon="right-long" %}
 Enterprise only. Requires a completed match or `runIncremental` phase before running.
@@ -40,7 +40,7 @@ Use the same arguments setup. All field definitions, pipes, `modelId`, and `zing
 args = EArguments()
 args.setModelId("your-model-id")
 args.setZinggDir("/tmp/models")
-# ... same field definitions and pipes as your original match run
+# 📊 ... same field definitions and pipes as your original match run
 ```
 
 ### Step 3: Wrap the arguments and set where the explain output goes
diff --git a/docs/interpreting-results/explainability-and-statistics.md b/docs/interpreting-results/explainability-and-statistics.md
index 184f4d1f1..ef9078f08 100644
--- a/docs/interpreting-results/explainability-and-statistics.md
+++ b/docs/interpreting-results/explainability-and-statistics.md
@@ -8,7 +8,7 @@ tags:
     primary: true
 ---
 
-# Explainability and Statistics
+# 📊 Explainability and Statistics
 
 {% hint style="info" icon="right-long" %}
 Enterprise only.
diff --git a/docs/interpreting-results/interpret-output-scores.md b/docs/interpreting-results/interpret-output-scores.md
index 7e4a7481d..88020bdef 100644
--- a/docs/interpreting-results/interpret-output-scores.md
+++ b/docs/interpreting-results/interpret-output-scores.md
@@ -4,7 +4,7 @@ description: >-
   to decide which clusters need human review.
 ---
 
-# Interpret Output Scores
+# 📊 Interpret Output Scores
 
 Every Zingg match or link output contains your original input fields plus a set of columns added by Zingg. These columns tell you which records have been resolved into the same entity, how confident the model is about each match, and (in Enterprise) provide a persistent identifier you can store downstream.
 
@@ -42,7 +42,7 @@ the next run.
 
 In the Enterprise version, `Z_CLUSTER` is replaced by the persistent `Zingg ID`. See `Zingg ID` in the next section.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: [Z Cluster ID and Zingg ID](../zingg-concepts/z-cluster-and-zingg-id.md)
 {% endhint %}
 
@@ -59,7 +59,7 @@ has both strong and weak matches - worth inspecting.
 
 Globally unique, persistent identifier for each resolved entity. It does not change between runs, including incremental runs. Safe to store and reference in downstream systems. Replaces `Z_CLUSTER` in Enterprise output.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full Zingg ID concept — [Z Cluster and Zingg ID](../zingg-concepts/z-cluster-and-zingg-id.md)
 {% endhint %}
 
@@ -71,7 +71,7 @@ The model confidence score is provided for each record pair. Higher values close
 
 Appears only in the link phase output. Identifies which source dataset each record came from. Use `Z_SOURCE` to trace each resolved record back to its origin system after linking two datasets together.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the link phase - [Link across datasets](../running-zingg/link-across-datasets.md)
 {% endhint %}
 
@@ -121,11 +121,11 @@ output.groupBy("ZINGG_ID") \
 {% endtab %}
 {% endtabs %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 Reading match output is the same in Community and Enterprise - only the cluster column name differs. Community produces `Z_CLUSTER`. Enterprise produces `ZINGG_ID`. Replace the column name in your code accordingly.
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Ordering clusters by `avg_min` ascending puts the weakest clusters at the top, those are the ones worth reviewing for false positives first.
 
 **Read more**:
@@ -184,7 +184,7 @@ A false positive is a cluster that contains records representing different real-
 
 ### Diagnosing false positives records that should not be together
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="lightbulb" %}
 Before debugging the similarity model, confirm the missed pairs are actually reaching it. If two records are in different blocks, the similarity model never evaluates them regardless of its accuracy.
 {% endhint %}
 
@@ -235,7 +235,7 @@ Review clusters with `Z_MINSCORE` of `0` manually to confirm the full cluster is
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * Z Cluster and Zingg ID - [Z Cluster ID and Zingg ID](../zingg-concepts/z-cluster-and-zingg-id.md)
diff --git a/docs/interpreting-results/output-statistics.md b/docs/interpreting-results/output-statistics.md
index b48ab2cb9..92173779b 100644
--- a/docs/interpreting-results/output-statistics.md
+++ b/docs/interpreting-results/output-statistics.md
@@ -8,9 +8,9 @@ tags:
     primary: true
 ---
 
-# Output Statistics
+# 📊 Output Statistics
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="building" %}
 Enterprise only. Output statistics are generated when the stats output pipe is configured in [Configure Zingg](../running-zingg/configure-zingg.md).
 {% endhint %}
 
@@ -55,7 +55,7 @@ args.setOutputStats(statsOutputPipe)
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="circle-info" %}
 The `$ZINGG_DYNAMIC_STAT_NAME` placeholder is automatically substituted with the statistics type (`SUMMARY`, `CLUSTER`, or `RECORD`) and a timestamp. This ensures each phase run writes to a separate statistics file. If `outputStats` is not configured, Zingg will not write statistics but the run will proceed normally.
 {% endhint %}
 {% endtab %}
diff --git a/docs/tuning/blocking-strategy.md b/docs/tuning/blocking-strategy.md
index aef8a4737..3ffbeb102 100644
--- a/docs/tuning/blocking-strategy.md
+++ b/docs/tuning/blocking-strategy.md
@@ -6,7 +6,7 @@ tags:
   - enterprise-only
 ---
 
-# Blocking Strategies: DEFAULT vs WIDER
+# 🎛️ Blocking Strategies: DEFAULT vs WIDER
 
 {% hint style="info" icon="right-long" %}
 **Enterprise only.** Controlled by the `blockingModel` config key (`DEFAULT` or `WIDER`, case-insensitive). If omitted, Zingg uses `DEFAULT`. Community/OSS has no such key - it always behaves like `DEFAULT`.
diff --git a/docs/tuning/configure-field-standardization.md b/docs/tuning/configure-field-standardization.md
index b52d54c20..d4396a054 100644
--- a/docs/tuning/configure-field-standardization.md
+++ b/docs/tuning/configure-field-standardization.md
@@ -8,7 +8,7 @@ tags:
     primary: true
 ---
 
-# Configure Field Standardization
+# 🎛️ Configure Field Standardization
 
 {% hint style="info" icon="right-long" %}
 **Enterprise** only. The Standardise Postprocessor is available in ZinggEC (Enterprise) and ZinggES (Enterprise Plus). Not available in Community.
diff --git a/docs/tuning/improve-accuracy/README.md b/docs/tuning/improve-accuracy/README.md
index 193c86a91..a75682fcb 100644
--- a/docs/tuning/improve-accuracy/README.md
+++ b/docs/tuning/improve-accuracy/README.md
@@ -4,7 +4,7 @@ description: >-
   enough.
 ---
 
-# Improve Accuracy
+# 🧪 Improve Accuracy
 
 When your Zingg match results contain false positives (records incorrectly grouped together) or false negatives (matching records not found), there are several ways to improve accuracy\
 without starting over.
diff --git a/docs/tuning/improve-accuracy/remove-stopwords-optional.md b/docs/tuning/improve-accuracy/remove-stopwords-optional.md
index ccf3f1933..055a6489d 100644
--- a/docs/tuning/improve-accuracy/remove-stopwords-optional.md
+++ b/docs/tuning/improve-accuracy/remove-stopwords-optional.md
@@ -4,7 +4,7 @@ description: >-
   high-frequency words that carry no matching signal.
 ---
 
-# Remove Stopwords (Optional)
+# 🎛️ Remove Stopwords (Optional)
 
 {% hint style="success" icon="right-long" %}
 This is an optional step. Run this between [Configure Zingg](../../running-zingg/configure-zingg.md) and [Create Training Data](../../running-zingg/create-training-data/) for best results. You can run it after matching has already started, but you will need to re-run the match phase for the stopwords to take effect. Most users skip this on the first run.
diff --git a/docs/zingg-concepts/identity-graph.md b/docs/zingg-concepts/identity-graph.md
index 63637bf55..cd21dda5c 100644
--- a/docs/zingg-concepts/identity-graph.md
+++ b/docs/zingg-concepts/identity-graph.md
@@ -6,13 +6,13 @@ tags:
   - ent
 ---
 
-# Identity Graph
+# 📘 Identity Graph
 
 Zingg produces an identity graph as its output. Nodes are records. Edges connect records that Zingg has identified as representing the same real-world entity. A cluster is a connected component in this graph, a group of records that all resolve to the same entity. Not all entity resolution systems work this way; some only produce pairwise match decisions without joining them into a graph. Zingg always produces the complete graph
 
 Zingg's identity graph is this structure. What differs between Community and Enterprise is not the graph itself but how it is managed over time.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 * **Open Source:** `Z Cluster`, non-persistent and may change between runs.
 * **Enterprise:** `Zingg ID`, stable, globally unique, persistent across incremental updates.
 {% endhint %}
@@ -31,12 +31,12 @@ In Enterprise, the identity graph is persistent. Once a `Zingg ID` is assigned t
 
 The incremental flow is, at its core, an engine for maintaining and updating this persistent identity graph as your data changes. Cluster merges, unmerges, and new record assignments all happen automatically. New runs do not override human-approved cluster decisions.
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Persistent identity graph with stable Zingg ID across runs is Enterprise only.\
 [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Community gives you a complete, accurate identity graph after every run.
 
 Enterprise gives you the same graph with stable `Zingg IDs` , so the graph can grow incrementally and downstream systems can reference it safely across time.
diff --git a/docs/zingg-concepts/z-cluster-and-zingg-id.md b/docs/zingg-concepts/z-cluster-and-zingg-id.md
index be96efa19..cd20c70e6 100644
--- a/docs/zingg-concepts/z-cluster-and-zingg-id.md
+++ b/docs/zingg-concepts/z-cluster-and-zingg-id.md
@@ -4,7 +4,7 @@ description: >-
   for every system that consumes Zingg's results.
 ---
 
-# Z Cluster and Zingg ID
+# 📘 Z Cluster and Zingg ID
 
 Zingg writes a cluster identifier into your match output so every record belonging to the same resolved entity carries the same value. Community uses `Z_CLUSTER`, a non-persistent identifier. Enterprise uses `Zingg_ID`, a persistent globally unique identifier. This page covers the difference between the two, the lifecycle behaviour, and how to use Zingg ID in your downstream systems.
 
@@ -33,7 +33,7 @@ If any downstream system needs a stable entity identifier to reference across ti
 
 </details>
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="building" %}
 Need stable entity IDs across runs? [Talk to us about Enterprise](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -65,7 +65,7 @@ Use this column to:
 * Track the same entity across incremental runs
 * Pass a stable entity reference to downstream CRM, analytics, or compliance systems
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more:**
 
 * [Interpret Output Scores](../interpreting-results/interpret-output-scores.md) - for the full reference of every Zingg output column
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
index fd51e496b..401d0ba01 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/README.md
@@ -28,7 +28,7 @@ Multiple match types can be assigned to a single field, separated by commas.
 | [`DONT_USE`](dont_use-match.md) | Appears in output but no computation is done. Not shown in the labeller when `showConcise` is `true`. | any | Record IDs, internal keys, any field needed in output but not in matching | All | A `customer_id` field that must appear in output for traceability but should not influence whether two records are the same entity. |
 | [`MAPPING_(FILENAME)`](mapping_-filename-match.md) | Maps input field values to canonical values using a user-supplied lookup file. Handles nicknames, abbreviations, company name variants, and categorical normalisation. Matching is tolerant to common variations defined in the mapping file. | string | Name fields (nicknames), company fields, categorical fields with different representations across systems | Enterprise only | "Jon", "Jonathan", "Johnny" all map to canonical "John" via nicknames.json. "M", "Male", "1" all map to "M" via gender.json. Matched before similarity scoring runs. |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read More:**
 
 Configure Zingg (full field definition setup in notebook 01) | Match types reference (Section 7) | Concepts glossary
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/dont_use-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/dont_use-match.md
index 5bf67b076..f928d7edc 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/dont_use-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/dont_use-match.md
@@ -13,7 +13,7 @@ description: >-
 `DONT_USE` fields are also hidden from the labeller when the `showConcise` flag is set to `true` —\
 so your domain experts see only the fields that are actually being used for matching when they label pairs.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `DONT_USE` performs no computation. There is no similarity algorithm to describe - the field is passed through to output unchanged.
 {% endhint %}
 
@@ -70,7 +70,7 @@ For example, a `region_code` field cheaply splits records into non-overlapping c
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `NULL_OR_BLANK` - for fields that should participate in matching but have frequent nulls
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
index 5b9a1cc9c..f7cf34eb3 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email-match.md
@@ -64,7 +64,7 @@ Use `EXACT` on the full email field when the domain matters.
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `EMAIL_OPTIMISED` - same as EMAIL, faster at production scale (ENT)
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
index 01016cb82..4fb5b5be4 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/email_optimised-match.md
@@ -46,11 +46,11 @@ performance matters. Customer datasets, patient registries, voter files—any do
 
 </details>
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 `EMAIL_OPTIMISED` is Enterprise only. The Community (open source) edition does not support this match type. Available in Enterprise Lite and above.
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `EMAIL` - use in Community or in development (all editions)
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
index 262dab08f..2ecc88d11 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/exact-match.md
@@ -86,7 +86,7 @@ Never use `EXACT` on name fields. Even internally consistent data has legitimate
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types:**
 
 * `FUZZY` - use when variation is expected
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
index 0748b8ee7..0329124f0 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md
@@ -95,7 +95,7 @@ Use `PINCODE` not `FUZZY` for postal codes. `PINCODE` is built to handle the spe
 
 
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related match types:**
 
 * `FUZZY_OPTIMISED` - same quality, better performance at scale
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md
index 151aae1b9..3d1cc1033 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md
@@ -18,11 +18,11 @@ The scoring pattern is exactly the same as `FUZZY` - their results are not affec
 
 For harder abbreviation cases like "IBM" vs "International Business Machines", `FUZZY_OPTIMISED` needs the same help `FUZZY` does - the strings are too dissimilar on their own, so combine with `MAPPING_(company_names)` to map the representative form explicitly.
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 `FUZZY_OPTIMISED` is Enterprise only. The Community (open source) edition does not support this match type. Available in Enterprise Lite and above.
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related Match types:**
 
 * `FUZZY` - same quality, use in development and evaluation
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
index 2d0212df9..22d05ca9a 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/mapping_-filename-match.md
@@ -25,7 +25,7 @@ The mapping file lists groups of equivalent values. `MAPPING` is typically combi
 3. **Value substitution** - each field value is checked against the dictionary, case-insensitively. A match gets replaced with its representative form; no match means the original value passes through untouched.
 4. **Similarity scoring** - the standardized values are then handed to whichever other match type is configured on the field - eg. `FUZZY`. That's where the real comparison happens.
 
-{% hint style="danger" icon="right-long" %}
+{% hint style="danger" icon="book-open" %}
 **Important:** `MAPPING_(FILENAME)` must be paired with another match type (eg. `FUZZY`, `EXACT` etc.). On its own, it only standardizes values - it doesn't score them.
 {% endhint %}
 
@@ -114,6 +114,6 @@ Dates should use `EXACT`. Different formats of the same date (`2024-01-15` vs `0
 
 
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 `MAPPING_(FILENAME)` is Enterprise only. The Community edition does not support mapping-based match types. Available in Enterprise Lite and above.
 {% endhint %}
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
index 2a882d254..61565a033 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/null_or_blank-match.md
@@ -68,7 +68,7 @@ If a field is universally null across all records with no non-null values to con
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 `NULL_OR_BLANK` common combinations:
 
 * `FUZZY`, `NULL_OR_BLANK` - name and address fields often null
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
index a52167110..f967f181a 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md
@@ -67,7 +67,7 @@ For "16gb", "500ml", and similar specifications where the unit matters as much a
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related matches:**
 
 * `NUMERIC_WITH_UNITS` - for product codes that include units (16gb, 500ml)
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
index f9d689888..69363f8c2 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md
@@ -56,7 +56,7 @@ Use `NUMERIC` for address fields. "42 Main Street" does not have units. `NUMERIC
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `NUMERIC` - for numbers without unit
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
index d3d85fbe8..3441af089 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_exact-match.md
@@ -78,7 +78,7 @@ Use `ONLY_ALPHABETS_FUZZY` instead when the alphabetic component also has spelli
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `ONLY_ALPHABETS_FUZZY` - use when alphabetic parts also have variation
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
index 0709c3170..300c90834 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy-match.md
@@ -98,7 +98,7 @@ If your address data is structured enough that `FUZZY` on the full string gives
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `NUMERIC` - use this for numeric fields
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
index 1008e7fac..bc5fa612c 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/only_alphabets_fuzzy_optimised-match.md
@@ -45,11 +45,11 @@ Any field where you would use `ONLY_ALPHABETS_FUZZY` - street addresses, product
 
 </details>
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 `ONLY_ALPHABETS_FUZZY_OPTIMISED` is Enterprise only. The Community (open source) edition does not support this match type. Available in Enterprise Lite and above.
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types**:
 
 * `ONLY_ALPHABETS_FUZZY` - same matching quality, but more time
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
index cddb65cb9..f9d989e3a 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/pincode-match.md
@@ -59,7 +59,7 @@ If your postal code format uses a hyphen where both sides carry meaning, use `EX
 
 </details>
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related types:**
 
 * `EXACT` - use when format is guaranteed consistent
diff --git a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
index 623622076..2839cc83e 100644
--- a/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
+++ b/docs/zingg-concepts/zingg-configuration/field-definition/match-types/text-match.md
@@ -67,7 +67,7 @@ If your free-text fields have typos and spelling errors, `FUZZY` handles those b
 
 </details>
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 **Related types:**
 
 * `FUZZY` - better for short fields and when typos are present
diff --git a/docs/zingg-concepts/zingg-phases.md b/docs/zingg-concepts/zingg-phases.md
index bfb4b650d..3f63610cf 100644
--- a/docs/zingg-concepts/zingg-phases.md
+++ b/docs/zingg-concepts/zingg-phases.md
@@ -2,13 +2,13 @@
 description: Steps in the entity resolution pipeline
 ---
 
-# Zingg Phases
+# 📘 Zingg Phases
 
 Zingg programs run different aspects of the entity resolution pipeline. Each phase covers one aspect of the pipeline. Here are the phases Zingg defines. 
 
 <details>
 
-<summary><strong><code>findTrainingData</code></strong></summary>
+<summary><strong><code>findTrainingData</code></strong> — <strong>scans your dataset and selects the most informative candidate pairs for labeling</strong></summary>
 
 Scans your dataset and selects the most informative candidate record pairs for labeling - edge cases where the model has the most to learn. Candidate pairs are written within the warehouse/lakehouse based on your configuration. 
 
@@ -16,7 +16,7 @@ Scans your dataset and selects the most informative candidate record pairs for l
 
 <details>
 
-<summary><strong><code>label</code></strong></summary>
+<summary><strong><code>label</code></strong> — <strong>presents candidate pairs for human review (Match / No Match / Uncertain)</strong></summary>
 
 Loads the candidate pairs from `findTrainingData` and presents them in a labeling widget. You mark each pair as Match, No Match, or Uncertain. 
 
@@ -24,7 +24,7 @@ Loads the candidate pairs from `findTrainingData` and presents them in a labelin
 
 <details>
 
-<summary><strong><code>train</code></strong></summary>
+<summary><strong><code>train</code></strong> — <strong>builds blocking and similarity models from labeled pairs; models persisted and reusable</strong></summary>
 
 Builds the blocking and similarity models from your labeled pairs. Both models are persisted to the customer environment. Once trained, the model can be reused on new data without retraining.
 
@@ -32,7 +32,7 @@ Builds the blocking and similarity models from your labeled pairs. Both models a
 
 <details>
 
-<summary><strong><code>match</code></strong></summary>
+<summary><strong><code>match</code></strong> — <strong>applies trained model to full dataset, writes resolved clusters (deduplication within one dataset)</strong></summary>
 
 Applies the trained model to your full dataset and writes resolved clusters to the output location. This is the phase that produces deduplicated, entity-resolved output for a single dataset.
 
@@ -40,7 +40,7 @@ Applies the trained model to your full dataset and writes resolved clusters to t
 
 <details>
 
-<summary><strong><code>link</code></strong></summary>
+<summary><strong><code>link</code></strong> — <strong>matches records across two or more separate datasets; outputs `Z_SOURCE` column</strong></summary>
 
 Like `match`, but for linking records across two or more separate datasets. Each output record carries a `Z_SOURCE` column indicating which source dataset it came from. Use when you want to find the same entity across datasets without merging them into one.
 
@@ -48,7 +48,7 @@ Like `match`, but for linking records across two or more separate datasets. Each
 
 <details>
 
-<summary><strong><code>runIncremental</code> (Enterprise)</strong></summary>
+<summary><strong><code>runIncremental</code> (Enterprise)</strong> — <strong>updates identity graph with new/changed records without retraining; inherits stable Zingg IDs</strong></summary>
 
 Updates the identity graph with new or changed records without retraining the model. Records that match an existing cluster inherit its Zingg ID. Records that do not match any existing cluster receive a new Zingg ID.
 
@@ -56,7 +56,7 @@ Updates the identity graph with new or changed records without retraining the mo
 
 <details>
 
-<summary><strong><code>explain</code> (Enterprise)</strong></summary>
+<summary><strong><code>explain</code> (Enterprise)</strong> — <strong>shows how a cluster was formed, pair scores, transitive connections; for governance/audit</strong></summary>
 
 Shows how a specific cluster was formed, which record pairs were compared, their similarity scores, and how transitive matching connected records through intermediate pairs. Used for governance, audit, and stakeholder review.
 
@@ -64,7 +64,7 @@ Shows how a specific cluster was formed, which record pairs were compared, their
 
 <details>
 
-<summary><strong><code>generateDocs</code></strong></summary>
+<summary><strong><code>generateDocs</code></strong> — <strong>produces HTML docs of training pairs and model stats; for SME review and audit</strong></summary>
 
 Produces HTML documentation of your labeled training pairs and model statistics. Useful for sharing with subject matter experts before training, or for audit and governance after.
 
@@ -72,7 +72,7 @@ Produces HTML documentation of your labeled training pairs and model statistics.
 
 <details>
 
-<summary><strong><code>verifyBlocking</code>(Enterprise)</strong></summary>
+<summary><strong><code>verifyBlocking</code> (Enterprise)</strong> — <strong>reports % of known matching pairs blocked together correctly; run before training</strong></summary>
 
 Reports what percentage of your known matching pairs are being blocked together correctly. Run after labeling and before training to catch blocking issues early.
 
@@ -80,7 +80,7 @@ Reports what percentage of your known matching pairs are being blocked together
 
 <details>
 
-<summary><strong><code>stopwords</code> recommend</strong></summary>
+<summary><strong><code>stopwords</code> recommend</strong> — <strong>analyzes column for high-frequency noise words (e.g., St, Ave, Inc, Ltd) to exclude from blocking</strong></summary>
 
 Analyses a specified column and returns a list of high-frequency words that should be treated as stopwords. Optional phase used before training when address or company name fields contain repeated noise words like 'St', 'Ave', 'Inc', 'Ltd'.
 
@@ -88,7 +88,7 @@ Analyses a specified column and returns a list of high-frequency words that shou
 
 <details>
 
-<summary><strong><code>transform</code> (Enterprise)</strong></summary>
+<summary><strong><code>transform</code> (Enterprise)</strong> — <strong>post-processes Zingg output for downstream apps; supports dictionary-based value substitution</strong></summary>
 
 Post processes Zingg output to consume in downstream applications. Supports disctionary based value substitution
 

From 66e347327d3b9b2a1223767b931357eb5f49e011 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 14:41:48 +0530
Subject: [PATCH 8/9] docs: restyle configuration-schema H1 and hints for issue
 #1363

---
 docs/reference/configuration-schema.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/reference/configuration-schema.md b/docs/reference/configuration-schema.md
index d40fcc327..77914db08 100644
--- a/docs/reference/configuration-schema.md
+++ b/docs/reference/configuration-schema.md
@@ -4,11 +4,11 @@ description: >-
   arguments.
 ---
 
-# Configuration Schema
+# ⚙️ Configuration Schema
 
 This page is the reference. Every Zingg configuration parameter, JSON key and Python API method is documented here with type, valid values, and edition availability.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the step-by-step task of setting up configuration - [Configure Zingg](../running-zingg/configure-zingg.md).
 {% endhint %}
 
@@ -94,7 +94,7 @@ At runtime, Zingg replaces the placeholder with the actual environment variable
 }
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Environment variable substitution applies anywhere in the JSON config - top-level parameters, pipe `props`, field paths, output locations, and credentials. The substitution is purely textual at runtime, so any environment variable Zingg can read can be referenced this way.
 {% endhint %}
 
@@ -119,14 +119,14 @@ For every JSON parameter, there is an equivalent Python API method on the `Argum
 | `primaryKey` (field-level) | `fieldDef.setPrimaryKey()` | Enterprise only | `recId.setPrimaryKey(True)` |
 | `postProcessors` (field-level) | `fieldDef.setPostProcessors()` | Enterprise only | `job_title.setPostProcessors([StandardisePostprocessorType("STANDARDISE", "jobtitles")])` |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Sample config files:
 
 * `github.com/zinggAI/zingg/tree/main/ examples/febrl/config.json`
 * `github.com/zinggAI/zingg/tree/main/examples/febrl120k/config.json`
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Related pages:**
 
 * [Configure Zingg](../running-zingg/configure-zingg.md) - step-by-step task of building your config

From b5f3b1940486f8391e2c0fc1cf3d2f2c60f99e36 Mon Sep 17 00:00:00 2001
From: Ishan Arora <105799425+inshane13@users.noreply.github.com>
Date: Sun, 30 Aug 2026 14:58:13 +0530
Subject: [PATCH 9/9] docs: restyle P6 Community/Enterprise/FAQ/Security for
 issue #1363

---
 .../community-vs-enterprise/README.md         |  4 ++--
 docs/community.md                             |  2 +-
 .../README.md                                 |  2 +-
 .../macos-setup-guide.md                      | 10 ++++-----
 .../ubuntu-wsl2-setup-guide.md                | 16 +++++++-------
 docs/faq.md                                   |  2 +-
 docs/zingg-python-api/community-python-api.md | 10 ++++-----
 .../enterprise-zinggec-python-api.md          | 22 +++++++++----------
 .../enterprise-zingges-python-api.md          | 10 ++++-----
 docs/zingg-python-api/working-with-python.md  |  6 ++---
 10 files changed, 42 insertions(+), 42 deletions(-)

diff --git a/docs/community-and-enterprise-editions/community-vs-enterprise/README.md b/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
index bf1789d92..c278a5f6f 100644
--- a/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
+++ b/docs/community-and-enterprise-editions/community-vs-enterprise/README.md
@@ -72,7 +72,7 @@ Enterprise clients are typically teams who run Zingg as a core data infrastructu
 
 </details>
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="circle-info" %}
 _Community → Enterprise_ is the move you make when you need persistent entity IDs, incremental updates, and matching that stays accurate as your data and models evolve.
 
 Enterprise Lite is for production with stability. Enterprise is for production with full lifecycle control.
@@ -83,7 +83,7 @@ Ready to move to Enterprise?
 * [Contact Us](https://www.zingg.ai/company/contact/contact)
 {% endhint %}
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Every record in Enterprise output receives a `Zingg_ID`, a GUID that persists across runs, incremental updates, and model upgrades.
 
 This is the foundational difference from Community, where `Z_Cluster` is non-persistent and cannot be safely stored downstream.
diff --git a/docs/community.md b/docs/community.md
index 3468f4bc8..6e7bfd781 100644
--- a/docs/community.md
+++ b/docs/community.md
@@ -4,6 +4,6 @@ description: >-
 nav_order: 8
 ---
 
-# Community
+# 📘 Community
 
 Be part of the conversation in the [Zingg Community Slack](https://join.slack.com/t/zinggai/shared\_invite/zt-w7zlcnol-vEuqU9m\~Q56kLLUVxRgpOA).
diff --git a/docs/contributing/setting-up-zingg-development-environment/README.md b/docs/contributing/setting-up-zingg-development-environment/README.md
index 4fd7ebdbf..f8f3b6708 100644
--- a/docs/contributing/setting-up-zingg-development-environment/README.md
+++ b/docs/contributing/setting-up-zingg-development-environment/README.md
@@ -4,7 +4,7 @@ description: >-
   locally. Step-by-step setup for macOS, Ubuntu, and WSL2
 ---
 
-# Setting Up Zingg Development Environment
+# 🎛️ Setting Up Zingg Development Environment
 
 Welcome to the Zingg development setup guide. To provide you with the most accurate instructions, please select your operating system:
 
diff --git a/docs/contributing/setting-up-zingg-development-environment/macos-setup-guide.md b/docs/contributing/setting-up-zingg-development-environment/macos-setup-guide.md
index dd5bade4c..fb866883d 100644
--- a/docs/contributing/setting-up-zingg-development-environment/macos-setup-guide.md
+++ b/docs/contributing/setting-up-zingg-development-environment/macos-setup-guide.md
@@ -5,7 +5,7 @@ description: >-
   source and run the full match workflow.
 ---
 
-# macOS Setup Guide
+# 🎛️ macOS Setup Guide
 
 This guide provides specific instructions for setting up the Zingg development environment on macOS.
 
@@ -19,7 +19,7 @@ Homebrew is the preferred package manager for macOS and makes installing system
 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **For Apple Silicon:** If `brew` is not found, run: `echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zshrc && source ~/.zshrc`
 {% endhint %}
 
@@ -29,7 +29,7 @@ Homebrew is the preferred package manager for macOS and makes installing system
 brew install git
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 It is suggested to fork the repository to your account and then clone the repository.
 {% endhint %}
 
@@ -60,7 +60,7 @@ rm spark-3.5.0-bin-hadoop3.tgz
 
 Make sure that Spark version you have installed is compatible with Java you have installed, and Zingg is supporting those versions.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Zingg supports Spark 3.5 and the corresponding Java version.
 {% endhint %}
 
@@ -117,7 +117,7 @@ mvn initialize
 mvn clean compile package -Dspark=sparkVer -Dmaven.test.skip=true
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Replace the `sparkVer` with the version of Spark you installed. For example, **-Dspark=3.5**. If you still face an error, include **-Dmaven.test.skip=true** with the above command.
 {% endhint %}
 
diff --git a/docs/contributing/setting-up-zingg-development-environment/ubuntu-wsl2-setup-guide.md b/docs/contributing/setting-up-zingg-development-environment/ubuntu-wsl2-setup-guide.md
index ec91123fd..8b73d148a 100644
--- a/docs/contributing/setting-up-zingg-development-environment/ubuntu-wsl2-setup-guide.md
+++ b/docs/contributing/setting-up-zingg-development-environment/ubuntu-wsl2-setup-guide.md
@@ -5,7 +5,7 @@ description: >-
   source and run the full match workflow.
 ---
 
-# Ubuntu/WSL2 Setup Guide
+# 🎛️ Ubuntu/WSL2 Setup Guide
 
 The following steps will help you set up the Zingg Development Environment on **Ubuntu/WSL2**.
 
@@ -31,7 +31,7 @@ wsl --install
 sudo apt update
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Follow this [tutorial](https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-windows-10#1-overview) for more information.
 {% endhint %}
 
@@ -42,7 +42,7 @@ Follow this [tutorial](https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-wi
 3. Set up Git by following the [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-git-on-ubuntu-20-04).
 4. Clone the Zingg Repository: `git clone https://github.com/zinggAI/zingg.git`
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 It is suggested to fork the repository to your account and then clone the repository.
 {% endhint %}
 
@@ -88,7 +88,7 @@ sudo mv spark-3.5.0-bin-hadoop3 /opt/spark
 
 Make sure that Spark version you have installed is compatible with Java you have installed, and Zingg is supporting those versions.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Zingg supports Spark 3.5 and the corresponding Java version.
 {% endhint %}
 
@@ -107,7 +107,7 @@ cd bin
 ./mvn --version
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Make sure that mvn -version should display correct java version as well(JAVA 11)
 
 Apache Maven 3.8.7
@@ -133,7 +133,7 @@ export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin:$JAVA_HOME/bin
 
 `<path_to_zingg>` will be a directory where you clone the repository of the Zingg. Similarly, if you have installed spark on a different directory you can set **SPARK\_HOME** accordingly.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Skip exporting `MAVEN_HOME` if multiple maven version are not required.
 {% endhint %}
 
@@ -150,7 +150,7 @@ echo $PATH
 mvn --version
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 If you have already set up **JAVA\_HOME** and **SPARK\_HOME** in the steps before you don't need to do this again.
 {% endhint %}
 
@@ -176,7 +176,7 @@ mvn initialize
 mvn clean compile package -Dspark=sparkVer -Dmaven.test.skip=true
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Replace the `sparkVer` with the version of Spark you installed. For example, **-Dspark=3.5** you still face an error, include **-Dmaven.test.skip=true** with the above command.
 {% endhint %}
 
diff --git a/docs/faq.md b/docs/faq.md
index 96d1b5cf2..61b37ef90 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -4,7 +4,7 @@ description: >-
 nav_order: 9
 ---
 
-# Frequently Asked Questions
+# 📘 Frequently Asked Questions
 
 ## How much training is enough?
 
diff --git a/docs/zingg-python-api/community-python-api.md b/docs/zingg-python-api/community-python-api.md
index fb09f993d..8f89795e6 100644
--- a/docs/zingg-python-api/community-python-api.md
+++ b/docs/zingg-python-api/community-python-api.md
@@ -4,7 +4,7 @@ description: >-
   build pipes, and run every Community phase from Python.
 ---
 
-# Community Python API
+# 📘 Community Python API
 
 The Community Zingg Python API is the open source way to run Zingg from Python. It provides classes for arguments, field definitions, client options, pipes, and the Zingg execution client.
 
@@ -42,7 +42,7 @@ pip install zingg
 | `BigQueryPipe` | Read from or write to Google BigQuery. |
 | `SnowflakePipe` | Read from or write to Snowflake (via Spark connector). |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Full auto-generated method signatures for every class above are at the [Zingg Python OSS API reference on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonOss/markdown/zingg.client.md).
 {% endhint %}
 
@@ -85,7 +85,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For all match types and combinations → [Match Types](../zingg-concepts/zingg-configuration/field-definition/match-types/)
 {% endhint %}
 
@@ -133,7 +133,7 @@ genericPipe.addProperty("user", "your_user")
 genericPipe.addProperty("password", "your_password")
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For all connector formats and full pipe configuration → [Connect Your Data](../connect-your-data/pipes-and-data-connections.md)
 {% endhint %}
 
@@ -277,6 +277,6 @@ zingg.initAndExecute()
 
 Working example notebooks are at `github.com/zinggAI/zingg/tree/main/examples/febrl`. Start with `FebrlExample.py`.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Auto-generated API reference for every class and method: [Zingg Community Python API on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonOss/markdown/zingg.client.md)
 {% endhint %}
diff --git a/docs/zingg-python-api/enterprise-zinggec-python-api.md b/docs/zingg-python-api/enterprise-zinggec-python-api.md
index e0a34403b..8022e47ce 100644
--- a/docs/zingg-python-api/enterprise-zinggec-python-api.md
+++ b/docs/zingg-python-api/enterprise-zinggec-python-api.md
@@ -12,14 +12,14 @@ tags:
 
 ZinggEC is the Zingg Enterprise Common Python API. It extends the Community API with features required for production identity resolution - persistent Zingg IDs, deterministic matching, pass-through, output statistics, incremental matching, cluster approval, and additional pipe types for Unity Catalog and in-memory data.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * [Community Python API](community-python-api.md) - Community (open source)
 * [Enterprise ZinggES Python API](enterprise-zingges-python-api.md) - Enterprise Plus with Spark client
 {% endhint %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="book-open" %}
 ZinggEC requires a Zingg Enterprise licence and the `zinggEC` package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact).
 {% endhint %}
 
@@ -48,7 +48,7 @@ pip install zinggEC
 | `EArguments` | `EArguments`, `DeterministicMatching` | Enterprise arguments object plus deterministic matching rules |
 | `EFieldDefinition` | `EFieldDefinition` | Enterprise field definition with primary key support and mapping match type |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Full auto-generated method signatures for every class are at the [Zingg Enterprise Common Python API reference on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonEC/markdown/zinggEC.md).
 {% endhint %}
 
@@ -106,7 +106,7 @@ fieldDefs = [
 args.setFieldDefinition(fieldDefs)
 ```
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 `setPrimaryKey(True)` marks the primary key field, required for `runIncremental` to track records correctly across runs.
 {% endhint %}
 
@@ -121,7 +121,7 @@ fname = EFieldDefinition("fname", "string", MatchType.FUZZY,
 
 The second argument to `MappingMatchType` is the mapping file name (without extension). The mapping file must be present in your working directory.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full mapping file format and rules → [Mapping match type](../zingg-concepts/zingg-configuration/field-definition/match-types/)
 {% endhint %}
 
@@ -141,7 +141,7 @@ args.setPassthroughExpr(
     "AND is_deceased IS NOT NULL")
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 Pass through excludes records from cluster formation. They still appear in the identity graph and output with their own Zingg ID. Zingg internally applies the negation of `passthroughExpr` to filter matching records - ensure the negative of your expression yields the records that are NOT pass-through.
 {% endhint %}
 
@@ -156,7 +156,7 @@ dm3 = DeterministicMatching('fname', 'stNo', 'lname')
 args.setDeterministicMatchingCondition(dm1, dm2, dm3)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For full deterministic matching concepts and configuration → [Deterministic vs Probabilistic Matching](../zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md)
 {% endhint %}
 
@@ -170,7 +170,7 @@ statsOutputPipe.setHeader("true")
 args.setOutputStats(statsOutputPipe)
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For statistics interpretation → [Output Statistics](../interpreting-results/output-statistics.md)
 {% endhint %}
 
@@ -354,7 +354,7 @@ zinggIncr = EZingg(incrArgs, incrOptions)
 zinggIncr.initAndExecute()
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 For the full incremental matching workflow → [Run Incremental Matching](../running-zingg/run-incremental-matching.md)
 {% endhint %}
 
@@ -400,7 +400,7 @@ zinggAppr = EZingg(apprArgs, apprOptions)
 zinggAppr.initAndExecute()
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: For the full cluster approval workflow → [Cluster Approval](../running-zingg/cluster-approval.md)
 {% endhint %}
 
@@ -485,6 +485,6 @@ zinggIncr = EZingg(incrArgs, incrOptions)
 zinggIncr.initAndExecute()
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Auto-generated API reference for every class and method: [Zingg Enterprise Common Python API on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonEC/markdown/zinggEC.md)
 {% endhint %}
diff --git a/docs/zingg-python-api/enterprise-zingges-python-api.md b/docs/zingg-python-api/enterprise-zingges-python-api.md
index ea3bd2898..b86119b45 100644
--- a/docs/zingg-python-api/enterprise-zingges-python-api.md
+++ b/docs/zingg-python-api/enterprise-zingges-python-api.md
@@ -13,14 +13,14 @@ ZinggES is the Zingg Enterprise Spark Python package. It provides the Spark clie
 
 Use this when you have an Enterprise Plus licence. The arguments, field definitions, pipes, and execution pattern are the same as ZinggEC; only the import package and Spark client classes differ.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * For Enterprise (and Enterprise Lite) without the Spark client - [Enterprise ZinggEC Python API](enterprise-zinggec-python-api.md)
 * For Community - [Community Python API](community-python-api.md)
 {% endhint %}
 
-{% hint style="warning" icon="right-long" %}
+{% hint style="warning" icon="book-open" %}
 ZinggES requires a Zingg Enterprise Plus licence and the `zinggES` package. [Contact Zingg to get access](https://www.zingg.ai/company/contact/contact)
 {% endhint %}
 
@@ -46,7 +46,7 @@ pip install zinggES
 
 `EZingg` is the standard Enterprise Plus client. `EZinggWithSpark` is for environments where a Spark session already exists (Databricks, Fabric notebooks) - it reuses that session instead of creating a new one.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 All ZinggEC classes (`EArguments`, `EFieldDefinition`, `ECsvPipe`, `UCPipe`, `IncrementalArguments`, `ApproverArguments`, `MappingMatchType`, etc) work identically with ZinggES. Only the Spark client class changes.
 {% endhint %}
 
@@ -153,10 +153,10 @@ zingg = EZingg(args, options)
 zingg.initAndExecute()
 ```
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: The full example shown on the [ZinggEC Python API reference](https://docs.zingg.ai/latest/working-with-python/working-with-python-enterprise/index) and the [ZinggES Python API reference](https://docs.zingg.ai/latest/working-with-python/working-with-python-enterprise/index-1) is identical, except for which Spark client package is imported.
 {% endhint %}
 
-{% hint style="info" icon="right-long" %}
+{% hint style="info" icon="book-open" %}
 Auto-generated API reference for `ESparkClient`, `EZingg`, `EZinggWithSpark`: [Zingg Enterprise Spark Python API on GitHub](https://github.com/zinggAI/zingg/blob/main/docs/pythonES/markdown/ESparkClient.md).
 {% endhint %}
diff --git a/docs/zingg-python-api/working-with-python.md b/docs/zingg-python-api/working-with-python.md
index e6815130c..791ad6529 100644
--- a/docs/zingg-python-api/working-with-python.md
+++ b/docs/zingg-python-api/working-with-python.md
@@ -10,7 +10,7 @@ Instead of configuring Zingg using JSON, you can use Python to build and run Zin
 
 The Python API is available in three editions, each with its own package and class names. The pattern is the same across all three: set up `args`, set `options` with the phase name, create the Zingg object, call `initAndExecute()`.
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 To run on a local machine, install Zingg from the release before running Zingg Python programs. The Python package can be installed via `pip install zingg`. Detailed auto-generated API documentation is available at [readthedocs.org/projects/zingg](https://readthedocs.org/projects/zingg/).
 {% endhint %}
 
@@ -22,7 +22,7 @@ To run on a local machine, install Zingg from the release before running Zingg P
 | **Enterprise (ZinggEC)** | `zinggEC` | Enterprise Lite or Enterprise — adds deterministic matching, blocking strategy, primary key, pass-through, mapping match types, incremental, output stats |
 | **Enterprise Plus (ZinggES)** | `zinggES` | Adds the Spark client (`EZingg`, `EZinggWithSpark`) used by Enterprise Plus distributions |
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**: Pick your edition and follow the sub-page below:
 
 * [Community Python API](community-python-api.md)
@@ -40,7 +40,7 @@ Every Zingg Python program regardless of edition, follows the same five-step pat
 4. **Set input and output pipes** - configure data sources and sinks
 5. **Set options and execute** - create `ClientOptions` with the phase name, instantiate the Zingg object, call `initAndExecute()`
 
-{% hint style="success" icon="right-long" %}
+{% hint style="success" icon="book-open" %}
 **Read more**:
 
 * [Configure Zingg](../running-zingg/configure-zingg.md) - the configuration that feeds into every Python API call