The Databricks CLI is a legacy command-line interface tool for interacting with the Databricks platform, built on top of Databricks REST APIs. The repository explicitly notes that this is no longer under active development and has been released as an experimental client. Databricks recommends users migrate to newer CLI versions 0.200 and above, available in a separate repository, or to use the dedicated Databricks SDK for Python if they have been leveraging the code as a Python SDK for API interactions.
The tool provides Python-based command-line access to Databricks workspace management, cluster operations, job scheduling, and notebook interactions. Installation is straightforward through pip, requiring Python 3.7 or higher. Authentication is configured through the databricks configure command, which stores credentials at ~/.databrickscfg and supports both username/password and authentication token methods. The CLI also supports multiple connection profiles, allowing users to manage credentials for different Databricks workspaces or accounts simultaneously.
GitGenius activity tracking shows the repository has received consistent engagement despite its legacy status. Across 110 tracked issues and pull requests, the median response latency was 3248.4 hours with a mean of 11438.8 hours, indicating that while responses do occur, they are infrequent given the experimental nature of the project. Feature requests dominate the issue tracker with 34 labeled items, followed by 17 bug reports and 15 UX-related issues. The most active contributor tracked by GitGenius is chrisst with 212 events, followed by nfx with 109 events and pietern with 22 events. The repository shares overlapping contributors with major projects including microsoft/vscode, microsoft/typescript, and rust-lang/rust, suggesting involvement from developers working across multiple ecosystems.
The CLI is classified across numerous categories reflecting its broad functionality: command-line interface, clusters, automation, workflows, cloud data platform, data engineering, workspace management, notebooks, machine learning, jobs, Spark, and big data analytics. This wide classification scope demonstrates the tool's role as a general-purpose interface to Databricks' multi-faceted platform.
Known issues documented in the repository include a TLSv1.2 compatibility problem on macOS, where the built-in Python version lacks proper TLS support. Users on macOS are directed to install Python through Homebrew to resolve this issue. The repository also provides Docker support, allowing users to build and run the CLI within containerized environments without local Python installation concerns.
Documentation is maintained through links to official Databricks and Azure Databricks documentation sites. The project includes build status and code coverage badges, indicating ongoing integration testing practices. Given its legacy status and the availability of newer alternatives, this repository primarily serves users maintaining existing workflows or those requiring access to specific legacy functionality not yet available in the newer CLI versions.
