Change data capture (CDC) pipelines look straightforward on paper: capture database changes, publish them to Kafka, and update downstream systems. The difficulty starts when events are duplicated, consumers restart, projections drift, or a team needs to replay months of history without corrupting the state it is trying to recover.A reliable CDC design has to account for those failure modes from the beginning. That means combining Kafka and Debezium with idempotent writes, deterministic projections, controlled replay workflows, reconciliation checks, and enough recovery evidence to explain what happened when something goes wrong.