What change data capture does
Change data capture, or CDC, exposes database changes for downstream processing. A common architecture reads a source database's change log and delivers events to another system.
The result is a flow of changes, not automatically a correct analytical table.
Think in states and events
An initial snapshot describes existing state. A change stream describes later modifications. The transition between the two must avoid losing changes or applying them inconsistently.
Read the connector's actual snapshot and recovery semantics before designing a consumer.
Make repeated delivery safe
Consumers can encounter an event more than once after retries or recovery. An idempotent operation produces the intended state even when the same event is applied again.
For example, an upsert keyed by source record identity and guarded by a suitable source version can be safer than blindly incrementing a running total for every delivered event.
The correct design depends on what the source metadata guarantees.
Keep ordering claims narrow
An ordered partition is not the same as a globally ordered stream. Transactions spanning several records can introduce additional requirements.
Define which events must preserve order and ensure that keys, partitioning, and the destination update logic support that requirement.
Plan for schema changes and deletes
A column rename, a changed type, or a deleted source row can break a pipeline that worked with yesterday's schema.
Maintain compatibility checks, quarantine unexpected events, and document how deletes are represented and applied.
Reconcile, even when the stream looks healthy
Monitor lag and failures, but also compare source and destination data. A pipeline can keep moving while producing incorrect results.
Start with row counts and selected aggregates, then add checks that represent important business invariants. Recovery is complete when the data is trustworthy, not merely when consumers restart.