Building a Production-Grade Change Data Capture (CDC) System with PySpark

credit : human ai

Change Data Capture (CDC) is a critical pattern in modern data engineering that enables efficient tracking and processing of data changes from source systems. In this comprehensive guide, we’ll implement a robust, production-grade CDC system using PySpark, complete with sample data generation, multiple CDC strategies, and advanced features like data quality validation and performance optimization.

Understanding Change Data Capture

CDC captures changes (inserts, updates, deletes) from source systems and applies them to target systems in a controlled, efficient manner. This approach is essential for:

• Maintaining data consistency across systems
• Enabling incremental data processing
• Supporting historical data analysis
• Reducing processing overhead compared to full data refreshes

Let’s build a complete CDC implementation from scratch, starting with sample data generation.

1. Creating Sample Datasets

First, we need realistic data to demonstrate our CDC implementation. Let’s create a script to generate an initial dataset with 50,000 customer records and delta datasets with various changes.