Change Data Capture Streaming Pipeline
Change Data Capture streams every database change from the transaction log as ordered events, keeping search indexes, caches, and downstream systems in sync without dual writes. Debezium, Kafka, and idempotent sinks make targets fully replayable across clouds.
Overview
Keeping search indexes, caches, and downstream services in sync with a primary database is a classic integration headache. Polling is slow and lossy; dual writes are error-prone. Change Data Capture (CDC) reads the database's transaction log and emits every insert, update, and delete as an ordered event stream. Downstream systems consume these events to stay current without touching the source application.
Use CDC to feed search engines, caches, data warehouses, and event-driven services from an existing system of record, especially during incremental migrations.
Components
- Debezium: log-based CDC connectors that capture row-level changes from the source database.
- Kafka: the durable event log carrying per-table change streams.
- Schema Registry: manages change-event schemas and evolution.
- Kafka Connect: sinks change events into target systems.
- PostgreSQL: the source system of record with logical replication enabled.
- Elasticsearch: a search index kept current from the change stream.
- Redis: a cache invalidated and refreshed by change events.
Data Flow
Debezium tails the PostgreSQL write-ahead log and publishes each committed change as an event to Kafka, preserving order per primary key. Sink connectors and stream consumers apply these changes to Elasticsearch for search and Redis for caching. Because events come from the transaction log, downstream state converges to the source without dual-write inconsistencies. The Kafka log enables replay to rebuild any target from scratch.
Scaling and Resilience
Kafka partitioning by key preserves per-entity ordering while allowing parallel consumption. Replication across clouds survives a regional or provider outage. Debezium tracks log offsets so it resumes exactly where it left off after a restart. Sink connectors are idempotent, applying changes safely on replay. Consumer lag is the primary health signal.
Security
The CDC connector uses a least-privilege replication role on the source database. Kafka enforces TLS, authentication, and per-topic ACLs. Sensitive columns can be masked or filtered before publishing. Cross-cloud links use private connectivity. The schema registry prevents breaking changes that would corrupt downstream targets.
Trade-offs and Alternatives
CDC couples the pipeline to the source database's log format and requires careful handling of schema changes, large transactions, and initial snapshots. It introduces eventual consistency between source and targets. For low-change-rate systems, scheduled ELT may be simpler. Application-level event publishing (the outbox pattern) is an alternative that decouples from the log but requires application changes. CDC excels at non-invasively synchronizing existing systems.