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Data Engineering Standards

This document defines Bayat's standards for data engineering, including data modeling, ETL patterns, data quality, data governance, and analytics implementation.

Table of Contents

Introduction

Data engineering is the discipline of designing, building, and maintaining the data infrastructure required for data extraction, transformation, and loading (ETL), as well as for advanced analytics, machine learning, and data-driven decision making. This document establishes standards that ensure consistency, reliability, scalability, and security across all Bayat data engineering initiatives.

Data Architecture

Architectural Patterns

  1. Multi-Layer Data Architecture:

    • Raw/Bronze Layer: Original source data without modification
    • Processed/Silver Layer: Cleansed, validated, and transformed data
    • Curated/Gold Layer: Business-ready, aggregated, and enriched data
    • Presentation Layer: Optimized for specific analytics use cases

  2. Architectural Principles:

    • Separation of storage and compute
    • Decoupling of ingestion, processing, and serving
    • Data as a product thinking
    • Metadata-driven architecture
    • Immutable data (append-only where possible)

  3. Reference Architecture:

    ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │     │                 │
│   Data Sources  │────▶│   Ingestion     │────▶│   Processing    │────▶│   Serving       │
│                 │     │                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘     └─────────────────┘
                              │                       │                       │
                              │                       │                       │
                              ▼                       ▼                       ▼
                        ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
                        │                 │     │                 │     │                 │
                        │   Raw Storage   │     │  Processed      │     │   Curated       │
                        │   (Bronze)      │     │  Storage        │     │   Storage       │
                        │                 │     │  (Silver)       │     │   (Gold)        │
                        └─────────────────┘     └─────────────────┘     └─────────────────┘
                              │                       │                       │
                              └───────────────────────┼───────────────────────┘
                                                      │
                                             ┌───────────────┐
                                             │               │
                                             │  Metadata     │
                                             │  Management   │
                                             │               │
                                             └───────────────┘

Data Modeling

Dimensional Modeling

  1. Dimensional Model Components:

    • Fact tables (measures of business processes)
    • Dimension tables (context for facts)
    • Conformed dimensions (shared across fact tables)
    • Slowly changing dimensions (Type 1, 2, 3)

  2. Schema Types:

    • Star schema (central fact table with dimension tables)
    • Snowflake schema (normalized dimensions)
    • Data vault (for enterprise data warehousing)
    • One Big Table (for specific analytics use cases)

  3. Naming Conventions:

    • Fact tables: fact_<business_process> (e.g., fact_orders)
    • Dimension tables: dim_<business_entity> (e.g., dim_customer)
    • Views: v_<purpose>_<entity> (e.g., v_daily_sales)
    • Use underscore as word separator
    • Use singular form for entity names
    • Use lowercase for all object names

  4. Data Types and Constraints:

    • Use appropriate data types for columns
    • Define primary keys for all tables
    • Implement foreign key constraints where applicable
    • Apply NOT NULL constraints for required fields
    • Use appropriate indexing strategies

Data Vault Modeling

For enterprise data warehousing, follow these Data Vault 2.0 principles:

  1. Core Components:

    • Hub tables (business keys)
    • Link tables (relationships)
    • Satellite tables (descriptive attributes)

  2. Naming Conventions:

    • Hub tables: hub_<business_entity> (e.g., hub_customer)
    • Link tables: link_<relationship> (e.g., link_customer_order)
    • Satellite tables: sat_<hub/link>_<descriptor> (e.g., sat_customer_details)
-- Hub example
CREATE TABLE hub_customer (
    customer_hk BINARY(16) NOT NULL,  -- Hash key
    load_date TIMESTAMP NOT NULL,
    record_source VARCHAR(100) NOT NULL,
    customer_bk VARCHAR(50) NOT NULL,  -- Business key
    PRIMARY KEY (customer_hk)
);

-- Link example
CREATE TABLE link_customer_order (
    customer_order_hk BINARY(16) NOT NULL,  -- Hash key
    load_date TIMESTAMP NOT NULL,
    record_source VARCHAR(100) NOT NULL,
    customer_hk BINARY(16) NOT NULL,
    order_hk BINARY(16) NOT NULL,
    PRIMARY KEY (customer_order_hk)
);

-- Satellite example
CREATE TABLE sat_customer_details (
    customer_hk BINARY(16) NOT NULL,
    load_date TIMESTAMP NOT NULL,
    load_end_date TIMESTAMP,
    record_source VARCHAR(100) NOT NULL,
    hash_diff BINARY(16) NOT NULL,
    customer_name VARCHAR(100),
    customer_email VARCHAR(100),
    customer_address VARCHAR(200),
    PRIMARY KEY (customer_hk, load_date)
);

ETL/ELT Patterns

General Principles

  1. Prefer ELT over ETL when working with cloud data platforms
  2. Implement idempotent processes that can be safely re-executed
  3. Design for failure and recovery
  4. Maintain data lineage through all transformations
  5. Use incremental processing where possible
  6. Document transformation logic clearly

Data Ingestion Patterns

  1. Batch Ingestion:

    • Full load: Complete reload of source data
    • Incremental load: Delta processing using watermarks
    • Change data capture (CDC): Track and propagate changes

  2. Streaming Ingestion:

    • Event-driven processing
    • Micro-batch processing
    • Stream-table joins

  3. Hybrid Approaches:

    • Lambda architecture: Batch + streaming
    • Kappa architecture: Streaming-first

Data Transformation Patterns

  1. Transformation Types:

    • Cleansing: Handle missing values, outliers, duplicates
    • Conforming: Standardize formats and values
    • Enhancing: Enrich with additional data
    • Aggregating: Summarize for analysis

  2. Transformation Frameworks:

    • SQL-based: For readability and maintainability
    • Code-based: For complex logic (Python, Scala)
    • Declarative: Using transformation frameworks

Example dbt model:

-- dbt transformation model
{{
  config(
    materialized='incremental',
    unique_key='order_id',
    partition_by={
      "field": "order_date",
      "data_type": "timestamp",
      "granularity": "day"
    }
  )
}}

WITH source_orders AS (
    SELECT * FROM {{ source('sales', 'orders') }}
    {% if is_incremental() %}
    WHERE order_date > (SELECT MAX(order_date) FROM {{ this }})
    {% endif %}
),

transformed_orders AS (
    SELECT
        order_id,
        customer_id,
        order_date,
        status,
        amount,
        CASE 
            WHEN amount > 1000 THEN 'high_value'
            WHEN amount > 500 THEN 'medium_value'
            ELSE 'low_value'
        END AS order_tier
    FROM source_orders
)

SELECT * FROM transformed_orders

Orchestration Patterns

  1. Workflow Design:

    • DAG-based pipeline design
    • Pipeline templates for common patterns
    • Parameterized workflows

  2. Scheduling:

    • Time-based: Cron-style scheduling
    • Event-driven: Triggered by data arrival
    • Dependency-based: Triggered by upstream completion

  3. Error Handling:

    • Retry policies
    • Failure notifications
    • Dead-letter queues for failed records

Data Quality

Data Quality Framework

  1. Data Quality Dimensions:

    • Completeness: Data contains all required values
    • Accuracy: Data reflects the real-world entity
    • Consistency: Data is consistent across datasets
    • Timeliness: Data is up-to-date
    • Validity: Data conforms to defined rules
    • Uniqueness: No unexpected duplicates

  2. Data Quality Rules:

    • Schema validation rules
    • Business domain rules
    • Cross-dataset consistency rules
    • Trend and anomaly detection rules

  3. Quality Monitoring:

    • Automated quality checks
    • Data quality dashboards
    • Alerting for quality issues
    • Data quality metrics tracking

Example data quality check (Great Expectations):

# Data quality check using Great Expectations
expect_column_values_to_be_between(
    column="order_amount",
    min_value=0,
    max_value=100000,
    mostly=0.99
)

expect_column_values_to_not_be_null(
    column="customer_id"
)

expect_table_row_count_to_be_between(
    min_value=1000,
    max_value=1000000
)

Data Validation Process

  1. Validation Types:

    • Input validation: Before ingestion
    • Process validation: During transformation
    • Output validation: Before serving
    • Cross-system validation: Reconciliation

  2. Validation Implementation:

    • Pre-load validations
    • Post-load validations
    • Continuous monitoring validations
    • Reconciliation validations

  3. Handling Data Quality Issues:

    • Reject invalid records
    • Quarantine suspicious data
    • Repair data issues using defined rules
    • Notify stakeholders of critical issues

Data Governance

Data Cataloging

  1. Metadata Management:

    • Technical metadata (schema, data types)
    • Business metadata (definitions, ownership)
    • Operational metadata (lineage, quality)

  2. Data Dictionary:

    • Standard business definitions
    • Data element descriptions
    • Valid values and ranges
    • Calculation methodologies

Example metadata entry:

# Example metadata entry
entity: customer
description: "A person or organization that has purchased our products"
owner: "Customer Data Team"
steward: "John Doe"
classification: "Confidential"
fields:
  - name: customer_id
    description: "Unique identifier for a customer"
    data_type: string
    constraints: "primary key, not null"
    example: "CUST-12345"
    
  - name: customer_email
    description: "Primary email address for customer communications"
    data_type: string
    constraints: "not null, unique"
    example: "[email protected]"
    pii: true
    masking_policy: "partial_mask"

Data Lineage

  1. Lineage Tracking:

    • Source-to-target mappings
    • Transformation documentation
    • Impact analysis capabilities
    • Column-level lineage

  2. Lineage Visualization:

    • Graphical representation of data flows
    • Upstream and downstream dependencies
    • Transformation details on demand

Data Access Control

  1. Access Policies:

    • Role-based access control (RBAC)
    • Attribute-based access control (ABAC)
    • Column-level security
    • Row-level security

  2. Data Masking and Anonymization:

    • Sensitive data identification
    • Masking techniques
    • Anonymization methods
    • De-identification standards

Analytics Engineering

Modern Data Stack

  1. Core Components:

    • Data ingestion tools
    • Cloud data warehouse
    • Data transformation (e.g., dbt)
    • BI and visualization
    • Metadata management

  2. Implementation Standards:

    • Modular analytics code
    • Version-controlled transformations
    • CI/CD for data models
    • Testing frameworks for analytics

Metrics Layer

  1. Metrics Definition:

    • Standard metric definitions
    • Reusable calculation logic
    • Dimensional slicing capabilities
    • Time grain specifications

  2. Implementation Approach:

    • Centralized metrics repository
    • Semantic layer configuration
    • Metrics documentation

Example metric definition:

# Metric definition example
metric:
  name: customer_lifetime_value
  description: "The total value a customer generates over their lifetime"
  calculation: "SUM(order_amount) OVER (PARTITION BY customer_id)"
  dimensions:
    - customer_segment
    - acquisition_channel
    - region
  time_grains:
    - day
    - month
    - quarter
    - year
  owners:
    - "Marketing Analytics Team"

Performance Optimization

Query Optimization

  1. Query Design Principles:

    • Filter early in the query
    • Limit data scanned
    • Use appropriate joins
    • Leverage materialized views
    • Apply indexing strategies

  2. Partitioning and Clustering:

    • Partition by date for time-series data
    • Cluster by frequently filtered columns
    • Balance partition size

  3. Caching Strategies:

    • Result caching
    • Materialized views
    • Query acceleration

Cost Optimization

  1. Resource Management:

    • Right-sizing compute resources
    • Auto-scaling configurations
    • Workload isolation

  2. Cost Control Measures:

    • Query quotas and budgets
    • Idle resource management
    • Storage optimization
    • Cost attribution by team/project

Security and Compliance

Data Security

  1. Encryption:

    • Data encryption at rest
    • Data encryption in transit
    • Key management

  2. Authentication and Authorization:

    • Multi-factor authentication
    • Service account management
    • Principle of least privilege

  3. Audit and Monitoring:

    • Access logs retention
    • Suspicious activity detection
    • Compliance reporting

Compliance Standards

  1. Regulatory Compliance:

    • GDPR, CCPA, HIPAA compliance
    • Data residency requirements
    • Retention and deletion policies

  2. Privacy by Design:

    • Data minimization
    • Purpose limitation
    • Data subject rights support

Tools and Technologies

Recommended Technology Stack

  1. Data Storage:

    • Cloud data warehouses: Snowflake, BigQuery, Redshift
    • Data lakes: S3, ADLS, GCS with open table formats (Delta Lake, Iceberg, Hudi)

  2. Data Processing:

    • Batch: Spark, dbt
    • Streaming: Kafka, Spark Streaming, Flink

  3. Orchestration:

    • Airflow, Dagster, Prefect

  4. Data Quality and Observability:

    • Great Expectations, dbt tests, Monte Carlo

  5. Transformation:

    • dbt, Spark, Dataflow

  6. Metadata and Governance:

    • Amundsen, DataHub, Collibra

Testing and Validation

Testing Framework

  1. Test Types:

    • Unit tests for transformations
    • Integration tests for pipelines
    • End-to-end tests for data flows
    • Performance tests

  2. Testing Methodology:

    • TDD for data transformations
    • Automated testing in CI/CD
    • Data contract testing

Example dbt test:

# dbt test example
version: 2

models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: customer_id
        tests:
          - not_null
          - relationships:
              to: ref('customers')
              field: id
      - name: order_amount
        tests:
          - not_null
          - positive_values

Documentation

Documentation Requirements

  1. Pipeline Documentation:

    • Purpose and business context
    • Source and target details
    • Transformation logic
    • Schedule and dependencies
    • Error handling

  2. Data Model Documentation:

    • Entity relationship diagrams
    • Table and column descriptions
    • Business rules
    • Usage examples

  3. Operational Documentation:

    • Monitoring procedures
    • Troubleshooting guides
    • Disaster recovery plans

Best Practices by Technology

dbt Best Practices

  1. Project Structure:

    • Follow dbt recommended project structure
    • Organize models by business domain
    • Use subdirectories for staging, intermediate, and mart models

  2. Model Design:

    • Use the staging, intermediate, mart pattern
    • Leverage CTEs for readability
    • Document models and columns
    • Apply appropriate materializations

Spark Best Practices

  1. Job Design:

    • Partition data appropriately
    • Minimize shuffles and broadcasts
    • Use appropriate join strategies
    • Cache when beneficial

  2. Resource Management:

    • Right-size executors
    • Set appropriate partitioning
    • Monitor and tune performance

Airflow Best Practices

  1. DAG Design:

    • Create modular, reusable DAGs
    • Implement appropriate retries
    • Use sensors wisely
    • Set reasonable timeouts

  2. Operational Practices:

    • Monitor DAG performance
    • Set up appropriate alerting
    • Document DAG dependencies