How does change data capture (CDC) work in ETL extraction?

Change Data Capture (CDC) in ETL extraction identifies and captures only the data that has changed in a source system since the last extraction. Instead of reloading entire datasets, CDC minimizes data transfer and processing by focusing on new, updated, or deleted records. This approach is critical for efficiency, especially in large-scale systems where full data dumps are impractical. CDC works by tracking changes at the source—such as database inserts, updates, or deletes—using mechanisms like transaction logs, timestamps, or triggers. These changes are then extracted and staged for transformation and loading into the target system, ensuring the ETL pipeline processes only relevant data.

CDC typically employs three methods: log-based, trigger-based, and timestamp-based tracking. Log-based CDC reads database transaction logs (e.g., MySQL’s binlog or PostgreSQL’s Write-Ahead Log) to detect changes. This method is efficient and non-intrusive, as it doesn’t require schema modifications. Trigger-based CDC uses database triggers to fire events on data changes, capturing details in shadow tables. While effective, triggers can add overhead to transactional systems. Timestamp-based CDC relies on columns like last_modified to identify new or updated records, but it struggles with deletions unless soft deletes are used. Each method has trade-offs: log-based is ideal for low latency, triggers offer precision but impact performance, and timestamp-based is simple but incomplete for certain operations.

CDC is particularly useful in scenarios requiring near-real-time data updates, such as financial systems or inventory management. For example, a retail company might use log-based CDC to stream inventory changes to a data warehouse, enabling real-time stock alerts. CDC also reduces strain on source systems by avoiding repeated full-table scans. Tools like Debezium (log-based) or cloud services like AWS Database Migration Service automate CDC implementation, handling complexities like log parsing and schema evolution. By isolating changed data, CDC optimizes ETL resource usage and ensures timely data synchronization, making it a cornerstone of modern data integration pipelines.