databricks-python-sdk

You can automate Databricks cluster and job management by using the Databricks Python SDK to programmatically control resources, orchestrate jobs, and handle SQL warehouse operations within your custom Python applications.

The best way to run Spark code locally against Databricks clusters is using Databricks Connect, which allows you to execute local Spark code directly on your remote Databricks workspace for streamlined development.

Yes, the Python SDK supports interacting with Unity Catalog objects and managing model serving endpoints, providing comprehensive programmatic control over your Databricks data governance and ML deployment workflows.

Databricks Connect and the Python SDK include specific patterns for asynchronous applications and error handling, ensuring robust execution when automating complex Databricks operations or building CI/CD pipelines.

You should use the Databricks REST API directly for operations not yet covered by the Python SDK, allowing direct API interaction to bridge functionality gaps when managing your workspace resources programmatically.

You can build and manage Delta Live Tables pipelines programmatically by leveraging the Databricks Python SDK, which provides code examples and patterns for orchestrating these data engineering workflows within your automation scripts.