etl-sync-job-builder

To build an incremental ETL pipeline with watermark tracking, you create a sync job using a watermark key to track progress and avoid reprocessing. This ensures only new or modified data is extracted moving forward.

Idempotent upsert in data synchronization safely applies updates without creating duplicates. This means running the same data sync operation multiple times yields the same final state, preventing data drift and duplication.

Retry with backoff handles transient failures in batch processing by automatically reattempting failed data sync operations with increasing delays. This makes your ETL workflows resilient to temporary network or source system issues.

Yes, change data capture hooks can be used for streaming data workflows. They capture changes from diverse data sources and propagate updates, satisfying requirements for both batch and streaming data synchronization.

The best way to prevent data drift in incremental data sync pipelines is implementing watermark tracking alongside idempotent upserts. This combination reliably tracks sync progress while safely applying updates without duplicates.