databricks-jobs

You create a Databricks Job by defining task keys and setting `run_if` behaviors like `ALL_SUCCESS` or `ALL_DONE` to establish multi-task DAG dependencies. This approach ensures dependent tasks execute correctly based on upstream task completion statuses.

Databricks Jobs orchestration supports multiple trigger types including cron schedules, periodic intervals, file arrival, table updates, and continuous execution. You configure these triggers during job creation to automate workflows based on your specific operational requirements.

The best way to chain Databricks Jobs is using the `run_job_task` feature, which allows you to trigger one job from another. For scaling out parallel processing within a single job, you can implement `for_each_task` to iterate across parameter sets.

Databricks Jobs orchestration supports multiple compute configurations including job clusters with autoscaling, existing clusters, and serverless compute. You select the appropriate compute type during job setup to match your workload performance and cost requirements.

You manage Databricks Jobs by configuring access permissions for users and groups, setting up notification alerts for failures, and monitoring job health through run statuses. These operational settings ensure reliable execution and visibility across your production workflows.