CDC (Change Data Capture)

Change Data Capture (CDC) is a technique used to identify and track changes (inserts, updates, and deletes) made to data within a source system, typically a database. CDC captures these changes in near real-time and makes them available to downstream systems or applications, enabling the synchronization of data between multiple systems without requiring full data reloads. This approach is widely used in data warehousing, ETL (Extract, Transform, Load) processes, real-time analytics, and data replication.

Core Components and Functionality
CDC systems involve several key components to ensure reliable and efficient data capture:

1. Source System: The database or data storage system where changes occur. Common sources include relational databases (e.g., MySQL, PostgreSQL), data lakes, and cloud storage platforms.
2. Capture Mechanism: CDC identifies changes in the source system through several techniques, such as:
   
   • Log-Based CDC: Reads changes directly from the database’s transaction log, capturing data changes as they are committed. This approach is commonly used in high-performance CDC systems, as it is non-intrusive and minimizes the load on the source database.  
   • Trigger-Based CDC: Uses database triggers to record changes in specific tables. While effective for monitoring specific data sets, this method can add overhead to the source database.  
   • Timestamp-Based CDC: Detects changes by comparing timestamp fields in source records, suitable for databases with well-structured timestamps on rows.
3. Change Delivery: After changes are captured, CDC systems package them for delivery to target systems. CDC tools typically create change events or streams, encoding changes in a structured format (e.g., JSON, XML) for compatibility with a wide range of destinations.
4. arget System: The system or platform where captured changes are applied. This can be another database, data warehouse, cloud storage, or real-time processing platform.

Types of CDC
CDC implementations generally fall into two categories:

• Real-Time CDC: Changes are captured and delivered to the target system as they occur, with minimal delay. Real-time CDC is essential for applications like real-time analytics, data synchronization, and monitoring.
• Batch CDC: Changes are aggregated and delivered at scheduled intervals. This approach is suitable for periodic updates, reducing the impact on the source database but with higher latency compared to real-time CDC.

Data Stream and Replication
In CDC, changes are typically delivered as a continuous data stream of events. Each change event includes metadata such as the type of change (insert, update, delete), the table or record affected, and a timestamp. These events can be applied in sequence on the target system, allowing accurate replication of source data in near real-time. CDC streams often use message queues or event streaming platforms like Apache Kafka, Amazon Kinesis, or Google Pub/Sub for reliable data delivery.

CDC enables incremental replication, where only modified records are sent to the target, drastically reducing data movement and improving efficiency compared to full table replication.

Integration with ETL and Data Lakes
CDC has become integral to modern ETL pipelines and data lakes, enhancing data freshness and reducing the workload on source systems. In ETL processes, CDC captures changes incrementally, making it easier to synchronize data between sources and destinations without requiring repeated full data extractions. In data lake environments, CDC ensures that data remains current by continuously updating changes from multiple sources, supporting both historical and real-time data access.

CDC is essential in scenarios where data consistency and freshness are critical across distributed systems, such as real-time analytics, cloud data migration, data warehousing, and data integration. Its ability to capture and propagate only the necessary changes makes CDC an efficient solution for maintaining synchronized data views, especially in high-volume or high-frequency data environments. By minimizing data movement and allowing for event-based data updates, CDC provides organizations with the agility to keep up with fast-evolving data landscapes.