CDC and Real-Time Pipeline Program Playbook

Change data capture (CDC) streams every insert, update, and delete from a source database to downstream systems in near real time. Log-based CDC reads the database transaction log directly, which is low-overhead and captures changes reliably without polling. This playbook builds CDC as a program to feed analytics, search indexes, caches, and event-driven services from operational databases without dual-writing in application code.

The defining challenge is consistency under failure: changes must arrive in order, exactly once where it matters, and survive connector restarts, backfills, and schema changes.

Phase-by-Phase

Source Assessment. Inventory source databases, evaluate CDC methods (log-based versus query-based), and define consistency and latency targets per consumer.

Capture Pipeline. Configure log-based CDC connectors, publish change events to an event backbone, and handle schema evolution through a registry so downstream consumers do not break.

Sink Integration. Build sink consumers that apply changes idempotently to targets such as a warehouse or search index, and reconcile against the source to prove correctness.

Operations and Resilience. Stand up monitoring for replication lag, define backfill and replay procedures for initial loads and recovery, and document failure recovery.

Team and Roles

A data or streaming architect owns the topology and consistency model. Data engineers build connectors and sink consumers. A DBA configures source logging and manages impact. SREs operate the pipeline and on-call. Backend engineers integrate event consumers.

Risks and Mitigations

Replication lag, ordering, schema drift, and consistency are the central risks. Mitigate with log-based capture, a schema registry, idempotent sinks, ordered partitioning, and continuous reconciliation against the source.

Success Criteria

Target low replication lag, high data completeness, effectively-once processing for critical sinks, and strong MTTR for pipeline incidents.

Tooling

Use log-based CDC connectors publishing to Kafka, a schema registry with Avro or Protobuf, Kubernetes for connector and consumer workloads, and Prometheus for lag and health monitoring.