Change Data Capture (CDC) in ETL extraction identifies and captures only the data that has changed in a source system since the last extraction. This avoids reprocessing entire datasets, which is inefficient for large or frequently updated systems. CDC works by monitoring source databases for inserts, updates, or deletions and passing these incremental changes to downstream ETL processes. Common methods include log-based tracking (using database transaction logs), trigger-based systems (where database triggers flag changes), timestamp-based filtering (tracking rows modified after a specific time), and diff comparisons (comparing current and previous snapshots). Log-based CDC is often preferred for its minimal performance impact on the source system.
In ETL pipelines, CDC streamlines extraction by reducing data volume and processing time. For example, a log-based CDC system might read PostgreSQL’s Write-Ahead Log (WAL) to detect changes, then forward only new or modified rows to the transformation layer. This approach is critical for near-real-time use cases like syncing an operational database with a data warehouse. Instead of nightly full-table scans, CDC enables continuous updates, improving freshness while minimizing resource usage. Tools like Debezium or AWS DMS often handle log parsing and change streaming, allowing ETL processes to focus on transforming and loading incremental data. This is especially useful for high-throughput systems, where reprocessing entire datasets would delay insights or overload infrastructure.
Key considerations when implementing CDC include source system compatibility (e.g., access to transaction logs), handling schema changes, and ensuring reliability. For instance, if a database doesn’t support triggers or logs, timestamp-based CDC might require careful management of “last updated” columns to avoid missing changes. Additionally, CDC must account for transactional consistency—capturing all changes in a transaction as a single unit to prevent partial updates. Latency is another factor: log-based CDC can introduce delays if logs aren’t processed promptly. Teams must also plan for edge cases, like deleted records or updates that don’t modify data (e.g., unchanged timestamp refreshes), to avoid unnecessary processing. Properly implemented, CDC balances efficiency with accuracy in ETL workflows.