Idempotent Data Pipelines: Reruns Without Side Effects
An idempotent pipeline gives the same output for the same input, no matter how many times you run it. This lets you safely retry failed jobs without side effects, which is crucial for scheduled batch inference or feature engineering tasks.
An idempotent pipeline produces the same result for the same input, no matter how many times it's executed. This allows you to safely retry failed jobs without corrupting data, a cornerstone of reliable systems. It's essential for batch inference jobs run by an orchestrator like Airflow, ensuring a failed daily run can be re-executed cleanly. The main footgun: assuming model training is perfectly idempotent. Hardware non-determinism and algorithmic randomness mean you may not get a bit-for-bit identical model on each run.
Read the original → hopsworks.ai
- #data engineering
- #mlops
- #pipelines
- #system design
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