Build a Change Data Capture (CDC) Pipeline Blueprint
A blueprint to build a log-based CDC pipeline with Debezium and Kafka: enable source transaction logs, deploy connectors with a schema registry, integrate idempotent sinks, and validate change propagation.
Overview
Change data capture (CDC) reads a database's transaction log and emits each insert, update, and delete as an event, so downstream systems stay current without polling or full reloads. It powers cache invalidation, search indexing, microservice data sync, the transactional outbox pattern, and zero-downtime migrations. This blueprint builds a CDC pipeline with Debezium and Kafka.
Phases
Assessment. Identify source databases and the changes consumers need. Confirm log-based CDC support: PostgreSQL logical replication (wal_level=logical), MySQL binlog (ROW format), or SQL Server/Oracle log access. Define delivery semantics (at-least-once with idempotent consumers).
Source setup. Enable the transaction log feature, create a replication user with least privilege, and configure replication slots/publications (Postgres) or binlog retention (MySQL). Avoid letting slots grow unbounded.
Connector deployment. Deploy Debezium on Kafka Connect. Configure the source connector per table/schema, choosing topic naming, snapshot mode (initial vs. incremental), and a schema registry for Avro/JSON Schema to handle evolution.
Sink integration. Consume change events with sink connectors or stream processors into targets: a search index, a cache, a warehouse, or another database. Apply data contracts so consumers handle the event envelope (before/after, op type) correctly.
Validation. Reconcile source and target row counts, test insert/update/delete propagation, and verify behavior across connector restarts and snapshots.
Key Risks and Mitigations
- Data consistency: Use at-least-once delivery with idempotent, upsert-style sinks; reconcile counts regularly.
- Schema drift: Register schemas and enforce compatibility so a source column change does not break consumers.
- Replication lag: Monitor connector lag and slot size; alert on growth and scale Connect workers or partitions.
Recommended Tooling
Debezium on Kafka Connect for log-based CDC; Apache Kafka with a schema registry; sink connectors or Kafka Streams/Flink for delivery; Prometheus/Grafana for lag and slot-size monitoring.
Success Metrics
Low end-to-end latency from commit to consumer, high data freshness in downstream systems, and sustained throughput under peak write load.
Prerequisites
Log-based CDC enabled on the source, a least-privilege replication user, a Kafka cluster with Connect and a schema registry, and a reconciliation plan for source-to-target validation.