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Endigest AI Core Summary
This post provides a practical migration guide for teams moving from Apache Airflow to Databricks Lakeflow Jobs as their native orchestrator.
•XComs for small metadata should be replaced with task values, while XComs carrying actual data should move to Unity Catalog tables or volumes
•File sensors and asset-based polling are replaced by built-in file arrival and table update triggers, shifting from polling-based to event-driven orchestration
•Airflow's execution date macros (ds, etc.) should be replaced with explicit parameters, and backfills handled via parameter ranges instead of scheduler-driven catchup
•Branching with @task.branch maps to condition tasks, and dynamic task mapping with expand() maps to for-each tasks in Lakeflow Jobs
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Python Asset Bundles enable programmatic job generation for teams that dynamically generate DAGs per table or SQL file
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Databricks
Google Cloud