What is idempotency in ETL?
Why does every Data Engineer obsess about idempotency?
Because pipelines fail constantly. An idempotent pipeline produces the same final state whether you run it once or twenty times — so you re-run it after a failure and move on. A non-idempotent pipeline forces you to manually clean up partial state every time something breaks.
An idempotent operation produces the same result whether you run it once or a hundred times. In ETL, this means you can re-run a failed pipeline safely without creating duplicates or wrong totals.
In real life pipelines fail constantly — network blips, source outages, server crashes. If your pipeline is idempotent, you just re-run it. If it is not, you face the nightmare of cleaning up partial data and figuring out exactly where the previous run failed. Idempotency is what separates a hobby script from production-grade DE.