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DP-750 · 4.1.1 · Deploy and maintain data pipelines and workloads

Pipeline operation order

Design order of operations for a data pipeline

Model dependencies from data requirements so independent work runs in parallel and dependent work starts only when prerequisites finish.

Read the workflow as a DAG

Microsoft Learn Lakeflow Jobs UI showing a multi-task job DAG with branching logic and a scheduled trigger.

The graph should represent real data and control dependencies. Independent work belongs on parallel branches; downstream work waits only for the prerequisites it actually needs.

Parallelize independent tasks

  1. 1Ingest AStarts immediately
  2. 2Ingest BStarts independently
  3. 3TransformWaits for both inputs
  4. 4PublishWaits for transformed result
Dependency-driven order

Task order should follow dependencies rather than the order in which tasks were created.

Finalize deliberately

  • Run only after successAll succeeded
  • Cleanup must always runAll done
Final task condition

Use status conditions for cleanup and notification rather than burying finalization logic inside notebooks.

Key points

  • Dependency: Defines which upstream tasks must finish first
  • Parallelism: Independent tasks can run concurrently
  • Run if: Controls execution from upstream task status
  • Cleanup: Use completion conditions for recovery or finalization tasks

Exam trap

Do not serialize independent tasks merely because they were created in that order.

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