databricks-jobs

You can create and schedule multi-task data engineering workflows by defining dependencies between notebooks, Python wheels, and SQL tasks using Databricks Asset Bundles. The skill scaffolds the project and configures periodic or cron-based schedules for deployment.

Scaffolding a Databricks Lakeflow Jobs project is done using the `databricks bundle init` command. This quickly initializes the project structure, allowing you to define data pipelines with task dependencies, parameters, and scheduling configurations.

Yes, deploying Lakeflow Jobs requires Databricks CLI version 0.292.0 or higher. You must have this CLI version installed to access the full functionality needed to build and deploy data engineering workflows via Asset Bundles.

Yes, you can configure Python wheel and SQL tasks within a single Databricks job definition. The skill supports defining multi-task workflows where you can mix notebooks, Python wheels, and SQL tasks with defined dependencies.

Parameterization for scheduled Databricks jobs works by defining variables within your Databricks Asset Bundle configuration. This allows you to pass dynamic values to notebook, Python wheel, or SQL tasks during scheduled or triggered workflow executions.

Using Databricks Asset Bundles instead of manual configuration streamlines the development and deployment of data engineering workflows. It enables efficient creation, parameterization, and management of scheduled jobs with multi-task dependencies directly from code.