build-cdc-streaming-pipeline

To build a CDC streaming pipeline, you extract changes directly from database transaction logs like WAL or binlog. This approach ensures high-fidelity, low-impact data propagation to downstream systems without the latency of traditional batch ETL processes.

Log-based CDC captures row-level changes, including deletions, directly from database transaction logs to maintain real-time data synchronization. This ensures all modifications are propagated accurately to downstream systems like search indexes or data warehouses immediately.

Ensuring exactly-once delivery in a CDC streaming pipeline involves managing complex stream integrity requirements. This includes correct consumer-group offset management, schema registry integration, and idempotent sink operations to prevent duplicate events during data propagation.

Yes, you can synchronize a production PostgreSQL database with an Elasticsearch search index in near-real-time. By leveraging log-based extraction from transaction logs, updates and deletes are reflected immediately without manual intervention.

Handling schema evolution and out-of-order events in a real-time data pipeline requires robust stream integrity management. Integrating a schema registry ensures compatibility, while correct offset management maintains event ordering during continuous streaming.

Batch ETL processes inherently introduce latency because they process data in scheduled intervals rather than continuously. Log-based CDC captures changes instantly from database transaction logs, providing the continuous, low-latency data propagation required for table mirroring.