What is CDC
CDC (Change Data Capture) is a method for detecting and capturing changes (inserts, updates, and deletes) in a database so that downstream systems or processes can react to those changes in near real-time.
How It Works
CDC tracks changes by examining write operations in the transaction log, a special table, or through triggers. When data in a source table changes, CDC records the event and relevant details—like old and new values—often in a dedicated change table or queue. Downstream consumers can then read these changes to keep data warehouses, caches, or microservices in sync without having to do full table scans.
Technical Details
Many database systems provide built-in CDC capabilities, such as SQL Server`s CDC feature or Oracle`s GoldenGate. In open-source ecosystems, tools like Debezium can monitor MySQL, PostgreSQL, or MongoDB transaction logs. CDC solutions typically store metadata like the transaction ID, time of change, and the before/after values. This allows near-real-time replication and event-driven architectures, improving latency and helping you avoid resource-intensive batch jobs.
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Best Practices
- Choose a CDC approach that aligns with your database and operational needs (e.g., built-in vs. external tools).
- Filter unneeded changes at the source if possible to reduce data traffic and downstream load.
- Implement robust error handling and retries, especially if consumers might temporarily go offline.
- Keep an eye on the transaction log or CDC tables to ensure archiving or cleanup so they don`t balloon in size.
Common Pitfalls
- Not cleaning up or managing historical CDC records, resulting in ever-growing storage usage.
- Failing to secure CDC data, which can expose sensitive changes to unauthorized consumers.
- Underestimating network or system overhead if changes are very frequent or run in large bursts.
- Relying on batch-based logic for data pipelines when a streaming or incremental approach is needed.
Advanced Tips
- Combine CDC with streaming platforms (e.g., Kafka) to build real-time event pipelines across multiple services.
- Use a publish-subscribe model where various applications or analytics services can tap into the same change feed.
- Leverage row-level or column-level filtering to fine-tune what changes are propagated.
- Consider using a schema registry or versioning approach if you track changes for evolving table structures.
