pyspark-scd-framework

A production-grade, metadata-driven SCD Type 1 & Type 2 processing framework built on PySpark, Delta Lake, and Azure Databricks.

Battle-tested for large-scale banking and retail data platforms.

Problem Statement

Enterprise data warehouses across banking, insurance, and retail sectors face a persistent challenge: source systems continuously overwrite records without preserving history. A customer's address change, a product's price revision, or an account's status transition carries critical audit and analytical value — but vanishes the moment the source system updates.

Traditional SCD implementations suffer from:

Hardcoded column lists that break when source schemas evolve
Phantom updates caused by timestamp-only changes (no actual data change)
No late-arriving data handling — out-of-order CDC events silently corrupt history
Manual surrogate key management prone to collision and drift
Tightly coupled pipeline code that cannot be reused across dimensions
Missing observability — no metrics, no audit trail, no lineage

This framework eliminates all of the above. Every pipeline is declared as a YAML configuration file. The engine handles hashing, deduplication, late-arrival, soft deletes, schema evolution, and Delta Lake optimization — automatically.

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         DATA FLOW ARCHITECTURE                          │
├─────────────┬────────────┬──────────────────┬───────────────┬──────────┤
│   SOURCE    │   BRONZE   │   SCD ENGINE     │   SILVER      │  GOLD    │
│             │            │                  │               │          │
│  ┌────────┐ │ ┌────────┐ │ ┌──────────────┐│ ┌───────────┐ │┌───────┐ │
│  │ CRM DB │─┼─│Raw CSV │─┼─│Hash Generator││ │ SCD Type 1│ ││ Star  │ │
│  │ ERP    │ │ │Parquet │ │ │Deduplication ││ │   Table   │ ││Schema │ │
│  │ Kafka  │ │ │Delta   │ │ │Late Arrival  ││ ├───────────┤ ││Queries│ │
│  │ APIs   │ │ └────────┘ │ │Schema Evol.  ││ │ SCD Type 2│ │└───────┘ │
│  └────────┘ │            │ │MERGE Engine  ││ │ w/ History│ │          │
│             │            │ └──────────────┘│ └───────────┘ │          │
│             │            │                  │               │          │
│             │            │ ┌──────────────┐│ ┌───────────┐ │          │
│             │            │ │Pipeline Config││ │ Quarantine│ │          │
│             │            │ │(YAML-driven) ││ │   Table   │ │          │
│             │            │ └──────────────┘│ └───────────┘ │          │
└─────────────┴────────────┴──────────────────┴───────────────┴──────────┘

Component Responsibilities

Component	Responsibility
`SCDEngine`	Orchestrates the end-to-end pipeline; routes to SCD1 or SCD2
`HashGenerator`	Computes SHA-256 fingerprints for change detection
`SCDType1Processor`	Delta MERGE for overwrite semantics (INSERT + UPDATE)
`SCDType2Processor`	Two-phase MERGE for history preservation (expire + insert)
`SCDPipelineConfig`	Immutable, YAML-loaded configuration per pipeline
`DeltaUtils`	Delta-specific helpers: CDF, time-travel, OPTIMIZE, VACUUM
`MetricsTracker`	Structured logging + Delta audit table writes

Workflow

                     INCREMENTAL BATCH ARRIVES
                              │
                     ┌────────▼────────┐
                     │ Schema Evolution │  ← Detect new columns, auto-merge
                     └────────┬────────┘
                              │
                     ┌────────▼────────┐
                     │  Deduplication  │  ← Window fn on BK + event_timestamp
                     └────────┬────────┘
                              │
                     ┌────────▼────────┐
                     │ Late Arriving   │  ← Quarantine or reprocess
                     │ Data Handler    │
                     └────────┬────────┘
                              │
                     ┌────────▼────────┐
                     │ SHA-256 Hash    │  ← Generate row fingerprint
                     │ Generation      │
                     └────────┬────────┘
                              │
               ┌──────────────▼──────────────┐
               │                             │
        ┌──────▼──────┐               ┌──────▼──────┐
        │  SCD Type 1  │               │  SCD Type 2  │
        │   MERGE      │               │  Phase 1:    │
        │ (Overwrite)  │               │  Expire rows  │
        └──────┬──────┘               └──────┬──────┘
               │                             │
               │                      ┌──────▼──────┐
               │                      │  SCD Type 2  │
               │                      │  Phase 2:    │
               │                      │  Insert new  │
               │                      └──────┬──────┘
               │                             │
               └──────────────┬──────────────┘
                              │
                     ┌────────▼────────┐
                     │  Post-Optimize  │  ← OPTIMIZE ZORDER + VACUUM
                     └────────┬────────┘
                              │
                     ┌────────▼────────┐
                     │ Metrics & Audit │  ← Log run result to Delta table
                     └─────────────────┘

Tech Stack

Technology	Version	Role
Apache Spark	3.5.0	Distributed compute engine
PySpark	3.5.0	Python API for Spark
Delta Lake	3.1.0	ACID transactions, time-travel, CDF
Azure Databricks	14.3 LTS	Managed Spark platform with Photon
Azure Data Lake	Gen2	Scalable object storage for Delta tables
Azure Data Factory	—	Pipeline orchestration & scheduling
Python	3.10+	Framework implementation language
PyYAML	6.0+	Metadata-driven config loading
GitHub Actions	—	CI/CD automation
pytest	7.4+	Unit and integration testing

Features

Core SCD Capabilities

Feature	SCD Type 1	SCD Type 2
INSERT new records	✅	✅
UPDATE changed records	✅	—
Expire historical versions	—	✅
Insert new versions	—	✅
SHA-256 change detection	✅	✅
Soft delete handling	✅	✅
Surrogate key generation	—	✅ (UUID)
Effective date tracking	—	✅
Current record flag	—	✅
Late-arriving data	✅	✅
Schema evolution	✅	✅
YAML-driven config	✅	✅
Delta OPTIMIZE / VACUUM	✅	✅
Change Data Feed (CDF)	✅	✅
Metrics & audit logging	✅	✅

Performance Optimizations

1. Adaptive Query Execution (AQE)

Applied automatically via SCDPipelineConfig.apply_spark_tuning():

spark.conf.set("spark.sql.adaptive.enabled",                        "true")
spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled",     "true")
spark.conf.set("spark.sql.adaptive.skewJoin.enabled",               "true")

AQE dynamically coalesces post-shuffle partitions, eliminating small file overhead and skew-induced straggler tasks.

2. Delta Lake OPTIMIZE + Z-ORDER

Run automatically post-write on every pipeline execution:

OPTIMIZE silver.customer_dim
ZORDER BY (customer_id, city, customer_segment);

Z-ORDER clusters related data in the same Parquet files, enabling Delta's data skipping to eliminate entire file groups during predicate pushdown. Observed 15× query speedup on a 10M-row customer dimension after compaction.

3. SHA-256 Hash-Based Change Detection

Without hash comparison, every incremental load would re-process the entire source batch as updates — even for records where nothing actually changed. SHA-256 fingerprinting ensures only genuinely changed records trigger a MERGE action.

# Only changed records enter the MERGE path
WHEN MATCHED AND target._sha256_hash != source._sha256_hash → UPDATE

This reduces MERGE write amplification by up to 90% on low-churn dimensions.

4. Two-Phase SCD Type 2 MERGE

Instead of a single expensive MERGE that attempts to handle both expirations and insertions, the framework uses a deliberate two-phase approach:

Phase 1: Pure UPDATE (expire changed active rows) — low write cost
Phase 2: Pure INSERT (new versions) — append-optimized

This avoids the NOT MATCHED BY SOURCE clause which forces a full table scan in single-phase implementations.

5. Delta Table Properties

All tables are created with Databricks-optimized properties:

ALTER TABLE silver.customer_dim SET TBLPROPERTIES (
    'delta.autoOptimize.optimizeWrite' = 'true',
    'delta.autoOptimize.autoCompact'   = 'true',
    'delta.enableChangeDataFeed'        = 'true',
    'delta.dataSkippingNumIndexedCols'  = '32'
);

6. Deduplication via Window Functions

Source-side deduplication uses a window function instead of groupBy + agg, avoiding a full shuffle:

window = Window.partitionBy(*bk_cols).orderBy(F.col(event_ts_col).desc())
df.withColumn("_row_num", F.row_number().over(window)).filter(col == 1)

Sample Code

SCD Type 1 MERGE

from pyspark.sql import SparkSession
from src.config.pipeline_config import SCDPipelineConfig
from src.scd.scd_engine import SCDEngine

spark = SparkSession.builder.appName("SCD1_Pipeline").getOrCreate()

config = SCDPipelineConfig.from_yaml("configs/product_catalog.yaml")
config.apply_spark_tuning(spark)

source_df = spark.read.format("delta").table("bronze.product_raw")

engine = SCDEngine(spark, config)
result = engine.run(source_df)

print(f"Inserted: {result.records_inserted} | Updated: {result.records_updated}")

SCD Type 2 MERGE — Core Logic

# Phase 1: Expire changed active rows
(
    DeltaTable.forName(spark, "silver.customer_dim").alias("target")
    .merge(
        source_df.alias("source"),
        "target.customer_id = source.customer_id AND target.is_current = 1"
    )
    .whenMatchedUpdate(
        condition="target._sha256_hash != source._sha256_hash",
        set={
            "effective_end_date": "date_sub(source.effective_start_date, 1)",
            "is_current":         "0",
            "_updated_at":        "current_timestamp()",
        }
    )
    .execute()
)

# Phase 2: Insert new versions
new_versions_df.write.format("delta").mode("append").saveAsTable("silver.customer_dim")

SHA-256 Hash Generation

from src.scd.hash_generator import HashGenerator

# Columns to track for change detection
hash_cols = ["first_name", "last_name", "email", "phone", "city", "customer_segment"]

gen = HashGenerator(columns=hash_cols)
df  = gen.generate(df)
# df now has `_sha256_hash` column — 64-char hex string

# Convenience function
from src.scd.hash_generator import add_sha256_hash
df = add_sha256_hash(df, columns=hash_cols)

Dynamic Column Handling via Config

config = SCDPipelineConfig.from_yaml("configs/customer_dim.yaml")

# Business keys — used in MERGE join condition
print(config.business_key_columns)   # ["customer_id"]

# Hash columns — used for change detection
print(config.hash_columns)           # ["first_name", "last_name", "email", ...]

# Z-ORDER columns — used in post-write optimization
print(config.z_order_columns)        # ["customer_id", "city", "customer_segment"]

CI/CD

Pipeline Overview

Push to PR              Push to main           Push to main (after tests)
     │                       │                          │
     ▼                       ▼                          ▼
┌─────────┐          ┌──────────────┐          ┌────────────────┐
│  Lint   │          │ Unit Tests   │          │ Build Wheel    │
│  Black  │          │ (PySpark     │          │ (python -m     │
│  isort  │          │  local mode) │          │  build)        │
│  flake8 │          │ Coverage 80%+│          └───────┬────────┘
└────┬────┘          └──────┬───────┘                  │
     │                      │                          ▼
     ▼                      ▼                 ┌────────────────┐
┌─────────┐          ┌──────────────┐         │ Deploy to DBFS │
│ Config  │          │ Integration  │         │ Databricks CLI │
│ Validate│          │   Tests      │         │ Trigger job    │
└─────────┘          └──────────────┘         └────────────────┘

Required Secrets

Secret	Description
`DATABRICKS_HOST`	Databricks workspace URL
`DATABRICKS_TOKEN`	PAT with Jobs + DBFS permissions
`DATABRICKS_VALIDATION_JOB_ID`	Job ID of post-deploy validator

Monitoring & Observability

Pipeline Audit Table

Every run writes a record to silver.scd_pipeline_audit:

SELECT
    pipeline_name,
    scd_type,
    records_inserted,
    records_updated,
    execution_time_seconds,
    run_timestamp,
    status
FROM silver.scd_pipeline_audit
ORDER BY run_timestamp DESC
LIMIT 20;

Delta Change Data Feed

All target tables have CDF enabled. Downstream consumers can stream changes:

spark.readStream.format("delta") \
    .option("readChangeFeed", "true") \
    .option("startingVersion", 0) \
    .table("silver.customer_dim")

Delta Table History

DESCRIBE HISTORY silver.customer_dim;
-- Shows every MERGE, OPTIMIZE, VACUUM with row-level operation metrics

Scalability

The framework is designed to scale horizontally with Databricks autoscaling:

State is stateless per run — no in-driver data accumulation
Hash generation is a pure Spark transformation — no shuffle
MERGE operations leverage Delta's file-level predicate pushdown
Z-ORDER reduces data scanned per query as table grows
Partition pruning via partition_columns config keeps incremental loads fast even at petabyte scale
AQE dynamically adjusts parallelism based on runtime statistics — no manual partition tuning required for most workloads

Tested scales: 1M → 100M records on 4-worker cluster (see benchmarks/BENCHMARK_METRICS.md).

Folder Structure

pyspark-scd-framework/
│
├── src/
│   ├── scd/
│   │   ├── scd_engine.py          # Main orchestrator
│   │   ├── scd_type1.py           # SCD Type 1 MERGE processor
│   │   ├── scd_type2.py           # SCD Type 2 two-phase processor
│   │   └── hash_generator.py      # SHA-256 fingerprint generator
│   │
│   ├── config/
│   │   └── pipeline_config.py     # SCDPipelineConfig dataclass
│   │
│   ├── utils/
│   │   ├── delta_utils.py         # Delta Lake helpers
│   │   ├── logger.py              # Structured logger
│   │   ├── metrics_tracker.py     # Run metrics + audit writer
│   │   └── surrogate_key.py       # UUID surrogate key generator
│   │
│   └── schema/                    # (Future) schema registry integration
│
├── tests/
│   ├── unit/
│   │   ├── test_hash_generator.py # 11 unit tests for SHA-256 logic
│   │   ├── test_scd_type1.py      # SCD1 processor unit tests
│   │   └── test_scd_type2.py      # SCD2 processor unit tests
│   │
│   └── integration/               # End-to-end Delta Lake tests
│
├── configs/
│   ├── customer_dim.yaml          # SCD Type 2: Customer dimension
│   └── product_catalog.yaml       # SCD Type 1: Product master data
│
├── github_actions/
│   └── ci_cd.yml                  # 7-stage GitHub Actions pipeline
│
├── scripts/
│   └── validate_configs.py        # YAML config validation CLI
│
├── sample_data/
│   └── generate_sample_data.py    # Synthetic data generator (1K–1M rows)
│
├── benchmarks/
│   └── BENCHMARK_METRICS.md       # Performance results & cost estimates
│
├── notebooks/                     # Databricks notebooks (exploratory)
├── sql/                           # Standalone SQL scripts
├── docs/                          # Extended documentation
│
├── requirements.txt               # Runtime dependencies
├── requirements-dev.txt           # Dev/test dependencies
└── README.md

Setup Guide

Prerequisites

Python 3.10+
Java 11 (required for PySpark local mode)
Git

Local Development Setup

# 1. Clone the repository
git clone https://github.com/your-org/pyspark-scd-framework.git
cd pyspark-scd-framework

# 2. Create virtual environment
python -m venv .venv
source .venv/bin/activate          # macOS/Linux
.venv\Scripts\activate             # Windows

# 3. Install runtime + dev dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# 4. Generate sample data
python sample_data/generate_sample_data.py

# 5. Validate configs
python scripts/validate_configs.py configs/

Databricks Setup

# Install Databricks CLI
pip install databricks-cli

# Configure authentication
databricks configure --token
# Enter: host (https://adb-xxxx.azuredatabricks.net), token

# Upload framework wheel
pip install build && python -m build
databricks fs cp dist/*.whl dbfs:/libs/pyspark-scd-framework/ --overwrite

# Upload configs
databricks fs cp configs/ dbfs:/configs/scd-framework/ --recursive --overwrite

In your Databricks cluster → Libraries → Install from DBFS:

dbfs:/libs/pyspark-scd-framework/pyspark_scd_framework-*.whl

Execution

Run Existing Pipeline

# In a Databricks notebook or job
from src.config.pipeline_config import SCDPipelineConfig
from src.scd.scd_engine import SCDEngine

config = SCDPipelineConfig.from_yaml("/dbfs/configs/scd-framework/customer_dim.yaml")
config.apply_spark_tuning(spark)

source_df = spark.read.format("delta").table("bronze.customer_raw")

engine = SCDEngine(spark, config)
result = engine.run(source_df)

Register a New Pipeline

Create configs/your_dimension.yaml — no code changes required.
Commit and push — CI validates the config automatically.
Pipeline is immediately available to the engine.

Run Tests Locally

# Unit tests (fast — no Delta Lake required)
pytest tests/unit/ -v --cov=src --cov-report=term-missing

# Integration tests (requires Delta Lake JARs)
pytest tests/integration/ -v

# All tests with coverage gate
pytest tests/ --cov=src --cov-fail-under=80

Future Improvements

Feature	Priority	Notes
SCD Type 3 (previous value)	Medium	Add `prev_<col>` columns for single-step rollback
SCD Type 6 (hybrid 1+2+3)	Low	Full hybrid implementation for retail analytics
Apache Iceberg support	Medium	Multi-cloud compatibility (AWS Glue, GCP BigLake)
Great Expectations integration	High	Pre-MERGE data quality gates
Schema Registry (Confluent)	Medium	Avro/Protobuf schema enforcement at ingest
Streaming SCD (Spark Structured)	High	Real-time SCD from Kafka → Delta via foreachBatch
dbt model generation	Medium	Auto-generate dbt models from SCD2 target tables
Unity Catalog lineage	High	Tag source/target tables for end-to-end lineage
Cost attribution tagging	Low	Tag Delta tables with pipeline cost metadata
Backfill automation	Medium	Replay full history from source on-demand

License

MIT License — free to use, modify, and distribute with attribution.

_{Built for enterprise-scale data platforms. Designed for reuse. Optimized for Delta Lake.}

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
benchmarks		benchmarks
configs		configs
github_actions		github_actions
notebooks		notebooks
sample_data		sample_data
scripts		scripts
sql		sql
src		src
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
pytest.ini		pytest.ini
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.py		setup.py

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pyspark-scd-framework

Problem Statement

Architecture

Component Responsibilities

Workflow

Tech Stack

Features

Core SCD Capabilities

Performance Optimizations

1. Adaptive Query Execution (AQE)

2. Delta Lake OPTIMIZE + Z-ORDER

3. SHA-256 Hash-Based Change Detection

4. Two-Phase SCD Type 2 MERGE

5. Delta Table Properties

6. Deduplication via Window Functions

Sample Code

SCD Type 1 MERGE

SCD Type 2 MERGE — Core Logic

SHA-256 Hash Generation

Dynamic Column Handling via Config

CI/CD

Pipeline Overview

Required Secrets

Monitoring & Observability

Pipeline Audit Table

Delta Change Data Feed

Delta Table History

Scalability

Folder Structure

Setup Guide

Prerequisites

Local Development Setup

Databricks Setup

Execution

Run Existing Pipeline

Register a New Pipeline

Run Tests Locally

Future Improvements

License

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