You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.

Two configurations are needed:

Task dependency with 'All succeeded' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to 'All succeeded' — this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.

Repair run for recovery: Lakeflow Jobs' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair — their results are reused as-is.

Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.