databricks-jobs

You can schedule Databricks jobs for daily ETL pipelines by setting flexible triggers based on time, file arrival, or table updates. This allows you to automate multi-task DAG execution at specific times or upon data events.

Yes, you can create multi-task DAGs in Databricks using various task types. The orchestration supports dependencies between notebooks, Python scripts, SQL queries, and dbt tasks within a single workflow.

Databricks job orchestration integrates directly with the Python SDK, CLI, and Asset Bundles. This allows you to programmatically manage the complete job lifecycle, including creation, updates, and deletion from your local environment.

The best way to manage the Databricks workflow lifecycle programmatically is by using the Python SDK or CLI. You can create, list, run, update, and delete jobs, while defining complex multi-task dependencies and triggers.

You trigger a Databricks job on file arrival or table update by configuring flexible triggers in the job settings. This event-driven orchestration ensures your workflows execute automatically when source data changes.

Yes, Databricks workflow orchestration supports continuous execution for jobs. This allows specific tasks to run continuously, ensuring uninterrupted processing for streaming or always-on data pipeline requirements.