You are orchestrating a 5‑task lakehouse workflow where Task A discovers a partition path (e.g., s3://raw/yyymmdd=) that downstream tasks must use. Finance wants ephemeral, policy‑restricted compute and parallel fan‑out where possible. Which Databricks pattern best fits?
1Times asked
May 2026Last seen
May 2026First seen
💡 Model Answer
Use Databricks Workflows with job clusters per task. Task A writes the discovered path to a job parameter or a widget. Subsequent tasks read that parameter and run on their own job‑cluster, allowing each to execute in parallel (fan‑out). This keeps compute isolated, respects policy restrictions, and eliminates the need for a shared global cluster.
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