OFICIAL Databricks Newsroom Gadgets · Aug 24, 2026

Run, debug, and scale Databricks workloads from your local IDE

What happened

Based on Databricks Newsroom · Aug 24, 2026

Databricks has introduced an SSH tunnel feature enabling developers to run, debug, and scale Python and SQL workloads directly from local IDEs or CLI using Databricks compute infrastructure.

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Key points

Connect your local editor or CLI to Databricks compute, workspace, and Unity Catalog for seamless data engineering and ML development.

The Databricks workspace is purposefully built for data analysis and data engineering.

However, you might prefer using local IDEs and the CLI to take advantage of your own tooling and coding agents like Cursor, Copilot, and Claude Code.

This is especially true when developing complex, large-scale pipelines or machine learning models.

Databricks now allows developers to connect local IDEs such as VS Code and Cursor or the command-line interface directly to Databricks compute resources. This update addresses previous limitations where non-Spark workloads and dependency synchronization with Databricks Runtime posed challenges. Users can now execute and debug Python and SQL tasks on real cluster infrastructure while retaining their preferred development environment. The integration supports Serverless, AI Runtime, and dedicated clusters, ensuring consistent performance and scalability.

The new SSH tunnel feature enables interactive execution and debugging of workspace files and notebooks from local editors or the CLI. Dependencies and files remain synchronized with Databricks Runtime and the workspace, eliminating version mismatches. Coding agents like Cursor, Copilot, and Claude Code can operate within the SSH tunnel, leveraging full workspace context for more effective development. This setup streamlines workflows for complex data pipelines and machine learning models by combining local tooling with Databricks’ compute capabilities.

Getting started with the SSH tunnel requires minimal setup, with users able to initiate connections via a single command using the Databricks CLI. The tunnel can also be launched directly from IDEs by specifying flags such as --ide vscode or --ide cursor. Alternatively, the most recent version of the Databricks IDE extension supports starting the tunnel, providing flexibility in how developers initiate the connection. These options cater to different workflow preferences while maintaining a consistent development experience.

The update includes additional features designed to enhance CLI and IDE usability as primary development environments. Developers can now operate at the frontier of data and machine learning engineering by pointing their tools at Databricks and running workloads against real compute resources. This approach ensures a fast development loop, combining local ergonomics with Databricks’ scalable infrastructure. For guidance on selecting the right tools, Databricks provides documentation on connecting from IDEs and CLI.

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