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5 CDC tools and the tradeoffs you should know
Choosing a Change Data Capture (CDC) tool requires prioritizing failure recovery and schema evolution capabilities over simple connector counts.
What: The Seattle Data Guy argues that teams should focus on how CDC tools handle broken destinations, duplicate events, and type fidelity during initial snapshots. Evaluation should include tests that simulate service failures to see how the system handles recovery and data consistency.
Why it matters: Many organizations choose CDC tools based on marketing-heavy feature lists, only to struggle when data skew or schema changes cause silent pipeline failures.
Takeaway: When selecting a CDC tool, run a 'broken-destination' test by intentionally sabotaging the target database to observe whether the CDC pipeline handles the resulting errors gracefully or duplicates events.
Decoder
- CDC (Change Data Capture): A set of software design patterns that detect and track row-level changes in a database so that these changes can be replicated in real-time to other systems.
Original article
CDC evaluation should start with failure behavior, not connector counts. Compare capture method, initial snapshots and backfills, schema evolution, delivery semantics, observability, deployment model, and who owns recovery. A production proof needs representative data and a broken-destination test, especially for type fidelity and duplicate handling.
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