CDC for Real-Time Data Warehousing

Real-time data warehousing has become a critical requirement for modern data-driven organizations. Traditional batch ETL processes that run nightly or hourly can no longer meet the demand for immediate insights. Change Data Capture (CDC) provides the foundation for building real-time data warehouses by continuously capturing and streaming database changes as they occur, enabling near-instantaneous analytics and reporting.

Understanding CDC in the Data Warehouse Context

Change Data Capture is a design pattern that identifies and captures changes made to data in a database, then delivers those changes to downstream systems in real-time or near-real-time. Unlike traditional full-table extracts that read entire datasets repeatedly, CDC tracks only the incremental changes—inserts, updates, and deletes—making it highly efficient for data warehousing scenarios.

In a data warehouse context, CDC serves as the bridge between operational databases (OLTP systems) and analytical databases (OLAP systems). By capturing changes at the source and streaming them to the warehouse, organizations can maintain synchronized, up-to-date analytical datasets without impacting source system performance or requiring large batch processing windows.

CDC operates through several mechanisms:

• Log-based CDC: Reads transaction logs from the source database (e.g., MySQL binlog, PostgreSQL WAL)

• Trigger-based CDC: Uses database triggers to capture changes into shadow tables

• Query-based CDC: Polls tables for changes using timestamp or version columns

Log-based CDC is generally preferred for data warehousing because it has minimal performance impact on source systems and captures all changes reliably without requiring schema modifications.

Incremental Loading Patterns

Incremental loading is the cornerstone of efficient real-time data warehousing. Rather than reprocessing entire datasets, incremental patterns apply only the changes captured by CDC to the target warehouse.

Append-Only Pattern

In this pattern, all CDC events are appended to the warehouse as immutable records. Each change creates a new row with metadata indicating the operation type and timestamp. This approach preserves complete history and simplifies stream processing, but requires additional logic for querying current state.

CREATE TABLE customer_changes (
    change_id BIGINT PRIMARY KEY,
    customer_id INT,
    name VARCHAR(255),
    email VARCHAR(255),
    operation_type VARCHAR(10), -- INSERT, UPDATE, DELETE
    change_timestamp TIMESTAMP,
    source_transaction_id VARCHAR(100)
);

Upsert Pattern

The upsert (update-or-insert) pattern applies CDC changes directly to warehouse tables, maintaining current state. Updates and inserts modify existing rows or create new ones, while deletes remove records. This pattern provides simpler querying but requires merge capabilities in the warehouse.

MERGE INTO customer_warehouse AS target
USING customer_cdc_stream AS source
ON target.customer_id = source.customer_id
WHEN MATCHED AND source.operation = 'UPDATE' THEN
    UPDATE SET name = source.name, email = source.email
WHEN MATCHED AND source.operation = 'DELETE' THEN
    DELETE
WHEN NOT MATCHED AND source.operation = 'INSERT' THEN
    INSERT (customer_id, name, email) VALUES (source.customer_id, source.name, source.email);

Slowly Changing Dimensions (SCD)

For maintaining historical context, SCD patterns track how dimension attributes change over time. Type 2 SCD creates a new row for each change with validity timestamps, enabling historical analysis while preserving current state.

Streaming Ecosystem Integration

Modern CDC pipelines leverage streaming platforms to decouple data capture from consumption, providing scalability, fault tolerance, and flexibility.

Architecture Components

A typical CDC streaming architecture includes:

Debezium captures changes from source databases by reading transaction logs and converting them to change events. It supports multiple databases (MySQL, PostgreSQL, MongoDB, Oracle, SQL Server) and produces standardized event formats.

Apache Kafka serves as the distributed event streaming platform, receiving CDC events from Debezium and buffering them for consumption. Kafka topics organize events by source table, and partitioning ensures ordering guarantees per key.

Apache Flink or other stream processors consume CDC events from Kafka, transform them as needed, and write to the data warehouse. Flink provides exactly-once processing semantics, stateful operations, and windowing capabilities essential for complex transformations.

Reference Architecture

┌─────────────────────────────────────────────────────────────┐
│                  CDC to Data Warehouse Pipeline              │
└─────────────────────────────────────────────────────────────┘

  ┌─────────────────┐
  │ Source Database │  Operational System
  │   (PostgreSQL)  │  (OLTP)
  └────────┬────────┘
           │ Transaction Log (WAL)
           ▼
  ┌─────────────────┐
  │    Debezium     │  Log-based CDC
  │   Connector     │  (Kafka Connect)
  └────────┬────────┘
           │ Change Events (JSON/Avro)
           ▼
  ┌─────────────────────────────────────┐
  │         Apache Kafka                │
  │  ┌──────────┐  ┌──────────┐        │  Event Streaming
  │  │customers │  │ orders   │  ...   │  Platform
  │  └──────────┘  └──────────┘        │
  └────────┬────────────────────────────┘
           │ Stream Processing
           ▼
  ┌─────────────────┐
  │  Apache Flink   │  Transformations
  │  Kafka Streams  │  - Filtering
  └────────┬────────┘  - Enrichment
           │           - Aggregation
           ▼
  ┌─────────────────┐
  │  Data Warehouse │  Analytical System
  │ Snowflake / BQ  │  (OLAP)
  └─────────────────┘

Debezium Configuration Example

{
  "name": "postgres-cdc-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres-db.example.com",
    "table.include.list": "public.customers,public.orders",
    "topic.prefix": "prod"
  }
}

This configuration captures changes from specific PostgreSQL tables and publishes them to Kafka topics with the prefix prod (e.g., prod.public.customers).

Governance and Visibility

In production CDC pipelines, governance and operational visibility become critical. Streaming governance platforms provide centralized monitoring and management for Kafka-based CDC infrastructures:

• Schema validation: Ensures CDC events conform to expected schemas before reaching the warehouse

• Data quality monitoring: Tracks event volumes, delays, and anomalies in CDC streams

• Access control: Manages which teams can consume specific CDC topics

• Pipeline observability: Visualizes data flows from source databases through Kafka to warehouses

This governance layer prevents data quality issues from propagating to analytical systems and provides the operational transparency needed to maintain SLAs for real-time reporting.

Data Lake Ingestion with CDC

CDC pipelines often feed both data warehouses and data lakes, creating a unified approach to real-time analytics across structured and semi-structured data.

Lake House Architecture

The lake house pattern combines data lake flexibility with warehouse performance. CDC events land initially in the data lake (S3, Azure Data Lake) in raw format, then incremental transformations promote data through bronze, silver, and gold layers.

Bronze Layer: Raw CDC events stored as Parquet or Avro files, partitioned by timestamp Silver Layer: Cleaned and deduplicated events with basic transformations applied Gold Layer: Business-aggregated datasets optimized for analytics

Technologies like Delta Lake, Apache Iceberg, and Apache Hudi provide ACID transactions and time-travel capabilities on lake storage, enabling upserts and deletes that traditional data lakes cannot handle efficiently.

Incremental Processing Strategy

# Example: Flink streaming job for CDC to Delta Lake
from pyflink.table import StreamTableEnvironment

t_env = StreamTableEnvironment.create(env)

# Define source, sink, and process CDC stream
t_env.execute_sql("""
    INSERT INTO customer_delta
    SELECT customer_id, name, email
    FROM customer_cdc
    WHERE operation IN ('INSERT', 'UPDATE')
""")

Operational Considerations

Implementing CDC for real-time data warehousing requires careful attention to operational aspects that impact reliability and performance.

Schema Evolution

CDC pipelines must handle schema changes in source databases without breaking downstream consumers. Use schema registries (Confluent Schema Registry, Apicurio) to version and validate schemas. Configure warehouses to support schema evolution through automatic column additions or versioned tables.

Backpressure and Lag Management

Monitor consumer lag between CDC event production and warehouse ingestion. High lag indicates backpressure—when the warehouse cannot keep up with change volume. Address this through:

• Horizontal scaling of stream processors

• Warehouse optimization (clustering, partitioning)

• Micro-batching to reduce write overhead

• Filtering irrelevant changes at the source

Disaster Recovery

CDC pipelines must support failure recovery without data loss. Kafka's retention policies should accommodate reasonable downtime, and checkpoint mechanisms in stream processors ensure exactly-once processing. Maintain runbooks for rebuilding state after catastrophic failures, including procedures for historical backfills from database snapshots.

Summary

Change Data Capture transforms traditional batch-oriented data warehousing into responsive, real-time analytics infrastructure. By capturing incremental changes through log-based mechanisms and streaming them via platforms like Kafka, organizations achieve near-instantaneous data availability without overwhelming source systems or warehouse resources.

Successful CDC implementation combines appropriate loading patterns (append-only, upsert, SCD) with robust streaming architectures built on Debezium, Kafka, and stream processors like Flink. Integration with data lakes through lake house architectures extends CDC benefits to both structured analytics and flexible exploration workloads.

Operational maturity requires governance tools for visibility and control, careful schema evolution strategies, and proactive monitoring of pipeline health. When implemented thoughtfully, CDC-based real-time data warehousing delivers the immediacy modern businesses demand while maintaining the reliability and accuracy that analytical systems require.

Sources and References

• Apache Kafka Documentation: https://kafka.apache.org/documentation/

• Debezium Documentation: https://debezium.io/documentation/

• Apache Flink CDC Connectors: https://nightlies.apache.org/flink/flink-cdc-docs-stable/

• Delta Lake Documentation: https://docs.delta.io/

• Slowly Changing Dimensions: Kimball, R. & Ross, M. (2013). The Data Warehouse Toolkit. Wiley.

• Change Data Capture Patterns: Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media.