Change Data Capture (CDC) is a technique used to track and capture changes in data as they happen in a database or data store. CDC allows applications to detect and react to changes (inserts, updates, and deletes) in real-time without needing to frequently query the entire dataset. This technique is essential for use cases like data replication, syncing databases, auditing, or driving real-time analytics.
Why Use Change Data Capture?
- Real-time data integration: CDC enables real-time syncing between databases, ensuring up-to-date information in multiple systems.
- Analytics: Helps push data changes to analytics systems, enabling real-time dashboards and reports.
- Data Migration: CDC is often used in data migration processes where data is moved incrementally between systems without downtime.
- Audit and Logging: Capturing every change to maintain a log for auditing and compliance purposes.
How Change Data Capture Works
- Database Logs (Log-based CDC):
- Transaction Logs: Most databases maintain transaction logs (WAL in PostgreSQL, binlog in MySQL) where changes are recorded. CDC reads these logs to capture changes.
- High Efficiency: This method is efficient because it doesn’t require querying the database for changes; it simply reads the logs where all changes are already recorded.
- Example Tools: Debezium (open-source CDC tool), AWS DMS, Oracle GoldenGate. -2 . Triggers (Trigger-based CDC): Database Triggers: Special triggers are placed on the tables to track changes. When data is updated, the trigger captures this change and writes it to a special CDC table or logs. Moderate Efficiency: Triggers may introduce overhead on the database, especially for high-throughput systems, as they fire on every write operation.
- Polling (Table Diff-based CDC):
-
- Polling for Changes: The system periodically queries the database to check for changed rows based on timestamps or versioning columns.
- Less Efficient: Polling the database constantly to detect changes can be resource-intensive, especially on large datasets.
-