Data pipelines fail in ways application code rarely does: late data, schema changes, and partial loads. Designing for those realities is what separates reliable pipelines from fragile ones.
Make tasks idempotent
Rerunning a task should produce the same result, not duplicate rows. Design every step so a retry is always safe.
Observe everything
- Track row counts and freshness, not just task success.
- Alert on data quality, not only on crashes.
- Keep run history so you can debug yesterday’s failure today.
A pipeline that fails loudly and recovers cleanly beats one that silently produces wrong numbers.