What is Change Data Capture?
CDC monitors source databases for changes and propagates those changes to downstream systems. For context management, CDC enables near real-time synchronization without the polling overhead and latency of traditional batch integration.
CDC Implementation Patterns
Log-Based CDC
Tools like Debezium read database transaction logs to detect changes. This approach has minimal impact on source systems, captures all changes including deletes, and preserves transaction ordering.
Trigger-Based CDC
Database triggers fire on data changes, writing change records to staging tables. Provides fine-grained control but adds overhead to source transactions and requires database modification access.
Query-Based CDC
Periodically query for changes using timestamps or sequence numbers. Simpler to implement but may miss intermediate changes and adds query load to source systems.
Context Synchronization Architecture
CDC events flow through a message bus to context processors. Processors transform changes into context format, handle conflicts when multiple sources update related context, and maintain consistency with exactly-once processing guarantees.
Operational Considerations
Plan for schema evolution—when source schemas change, CDC pipelines must adapt. Implement monitoring for replication lag and change volume. Design recovery procedures for when CDC consumers fall behind.
