You have an Azure Databricks job named Job1 that contains an ingestion task named Task1...

Two task-level settings solve this:

Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.

Task2 run condition set to 'All succeeded' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked — it never runs on data from a failed upstream ingestion. The dependency is declared in Task2's 'Depends on' setting in the job configuration.

These two settings are independent and composable. Task1's retry policy gives it multiple chances to recover. Task2's dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.