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What DAG design patterns make an ML orchestration pipeline reliable in Airflow or Dagster?

Anyone can wire tasks into a DAG. The signal is the patterns that keep it correct under retries and backfills: idempotency, data-aware triggering, and the asset model. Here is what separates a flaky pipeline from a trustworthy one.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Anyone can wire tasks into a DAG. The signal is the patterns that keep it correct under retries and backfills: idempotency, data-aware triggering, and the asset model. Here is what separates a flaky pipeline from a trustworthy one.

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