Change Data Capture Implementation for a Source System That Had Resisted It
The team had been doing nightly snapshots for years. Real-time downstream needs made that untenable.
A team whose downstream consumers needed near-current data from a transactional source system that had been replicated to the analytics environment via nightly snapshots for years. The snapshot pattern had worked when the downstream needs were daily; new product requirements demanded near-real-time freshness, and the snapshot architecture couldn't deliver it.
The source system was a heavily-used transactional database with strict performance requirements. The DBAs responsible for it had resisted previous attempts at change data capture because earlier proposals had carried real risk of impacting source system performance. The leadership wanted CDC implemented in a way that the DBAs would actually approve and that would deliver the freshness the downstream consumers needed.
A CDC implementation built around log-based change capture with explicit attention to source system safety.
The capture mechanism was log-based — reading the database's transaction log rather than polling tables. Log-based CDC adds essentially no query load to the source system because it reads what the database is already writing. The DBAs reviewed and approved this approach where they had blocked polling-based proposals.
The capture stream was published to Kafka with appropriate per-table topic structure. Downstream consumers subscribed to the topics they needed without each consumer creating independent load against the source system.
Schema changes in the source system were handled explicitly. The CDC layer detected schema changes from the log stream and surfaced them as events alongside the data; downstream consumers received the schema change notification before the schema-changed records arrived, allowing them to adapt without breaking.
A backfill mechanism was built for the historical data that predated the CDC implementation. Initial loads of each downstream consumer started from a point-in-time snapshot of the source and switched to the live CDC stream from the snapshot's transaction position. The transition was seamless and didn't lose or duplicate records.
Monitoring was instrumented for replication lag — the time between a source system change and its appearance in the downstream stream. The lag became measurable and alertable rather than being a source of consumer complaints.
Downstream freshness moved from "yesterday at midnight" to "current within seconds" for the consumers that needed it. The DBAs continued to approve the architecture because it didn't impose meaningful load on the source system. The team gained a foundation it could extend to other source systems as needs surfaced rather than rebuilding the pattern each time.