Change Data Capture

Change Data Capture separates an initial dataset from the changes that occur afterward. A pipeline commonly loads a consistent snapshot, records an associated source position, and then processes subsequent inserts, updates, and deletes. Change records typically include a record identifier, operation type, and ordering or checkpoint information; before-and-after values depend on the source configuration. Coordinating the snapshot with the change stream prevents gaps or incorrect results while applications continue writing.

Common capture methods include reading database transaction logs, using database triggers, and querying for records modified after a stored watermark. Log-based capture can reduce repeated table scans, but requires engine-specific configuration, privileges, and sufficient log retention. Triggers add work to source transactions. Timestamp polling is simpler in some environments, but needs reliable modification tracking, careful boundary handling, and a separate mechanism for hard deletes. Incremental polling does not necessarily preserve every intermediate change.

In cloud architectures, a capture component can publish changes to a durable stream, object storage, or a target database. Consumers then transform records and update analytical tables, caches, search indexes, or other derived stores. Choose streaming when freshness requirements justify continuously running infrastructure; use micro-batches or scheduled incremental processing when minutes or hours of delay are acceptable. CDC describes which changes are captured, not a guarantee of real-time processing or a particular ingestion frequency.

For a database migration, a typical approach is to convert incompatible schemas, perform a full load, and replicate ongoing changes until the target catches up. For example, a heterogeneous migration may require translating data types, stored procedures, and indexes separately from moving table data. Before cutover, validate the target, stop or redirect source writes, drain remaining changes, and confirm synchronization. CDC reduces the write interruption but does not automatically eliminate downtime or provide bidirectional conflict resolution. Compare ongoing replication costs with a simpler offline migration when downtime is acceptable.

For foundation-model augmentation, CDC can keep a vector store aligned with authoritative content. A consumer maps each changed document to stable document and chunk identifiers, regenerates embeddings for affected text, and upserts the resulting vectors. It must remove obsolete chunks when documents shrink and delete vectors when source documents are deleted. Metadata-only changes may not require new embeddings, but permission changes still require prompt propagation so retrieval does not expose unauthorized content. Model or chunking changes can require broader reindexing even when source data has not changed.

Design consumers for retries and duplicate delivery unless the entire pipeline provides stronger guarantees. Idempotent upserts, deletion handling, source-version checks, and checkpointing help prevent stale events from overwriting newer state. Ordering is often limited to a record or partition rather than the whole dataset, and a multi-row transaction may not be applied atomically downstream. Monitor end-to-end lag, failed events, throughput, and retained-log coverage. If an outage exceeds the available change history, rebuilding a snapshot may be necessary.

CDC is not a backup, a schema-conversion tool, or a synonym for file transfer. AWS Transfer Family can move files containing snapshots or exported change batches, but it does not itself discover database row changes. Protect capture credentials and change payloads because logs and before-images may contain sensitive values. Periodic reconciliation remains useful for detecting missed deletes, transformation defects, or drift that incremental processing alone does not reveal.