🛢️ Data Engineering · Batch Pipelines
Make batch pipelines rerunnable by design
Idempotent stages, partitioned writes, and backfill strategies so failures heal instead of corrupt.
intermediate~35 minData EngineersAnalytics EngineersPlatform Data Teams
Steps
- 1Key every write by logical date/partition; overwrite partitions, never append blind
- 2Design stages to be safely re-executable from any point of failure
- 3Separate extraction, transformation, loading with checkpoints between
- 4Backfill = same code, different parameter — prove it in staging first
- 5Emit per-run metrics: rows in/out, duration, null-rates into a meta table
- 6Alert on freshness SLA breach before stakeholders notice stale dashboards
Common Pitfalls
- ▲append-only loads duplicating on retry
- ▲One giant DAG task failing everything for one bad record
Commands
Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill pipelines-batch-pipeline-idempotencyInstall globally
$ npx skills add aniruddhaadak80/skills --skill pipelines-batch-pipeline-idempotency -gTags
#etl#idempotency#orchestration#data-engineering#pipelines-batch