Developer tools use cases

Databricks provides an ecosystem of tools to help you develop applications and solutions that integrate with Azure Databricks and programmatically manage Databricks resources and data.

This page has tool recommendations for common developer scenarios. For a complete overview of developer tools, see Develop on Databricks. For best practices, see Developer best practices on Databricks.

Tool	When to use
SSH tunnel Visual Studio Code and Cursor extension PyCharm Databricks plugin For other IDEs, use the Databricks CLI with Databricks Connect	Interactive development and debugging from a local IDE
Databricks CLI	Direct interaction with Databricks from the command line Shell scripting Experimentation Invoke the REST API directly Manage local authentication profiles Sync code from the IDE to the Databricks workspace
Declarative Automation Bundles (a feature of the CLI)	Manage workflows and deploy projects to Databricks Automate user-specific flows Apply CI/CD best practices Co-version, co-author, and co-deploy your resources and assets as one unit Supports the most common resources
Databricks Terraform provider	Provision resources outside of Databricks, such as cloud accounts, metastores, and account-level identities Administer and create Databricks workspaces and metastores Use with existing Terraform-based infrastructure as code and CI/CD workflows Enforce permissions Guarantee environment portability and disaster recovery Many supported resources
Databricks Python SDK Databricks JavaScript SDK Databricks Java SDK Databricks Go SDK Databricks R SDK	Application development Integrate with existing deployment systems Create custom Databricks workflows and web services
SQL drivers	Run SQL commands and scripts from client applications
Databricks REST API	Automate processes where an SDK in your preferred programming language is not available Access to almost all Databricks resources Advanced scenarios only