Summary
The CLI introduces a preview image push command for the Databricks Artifact Registry, preconfigures coding assistants in serverless SSH sessions, and adds expiration flags for Postgres branch creation. In Asset Bundles, clusters now support library configurations, Terraform deployments migrate to the direct engine prior to execution, and Postgres snapshot schedules are restricted to YAML.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/cli
AI Runtime commands have moved from experimental to databricks air, and SSH commands now support keeping detached background processes running after tunnel disconnect. Databricks Asset Bundles direct engine resolved multiple issues around unnecessary resource recreations, Unity Catalog grant convergence, and Git-sourced Python tasks.
Direct deployment state version 3 is a breaking change requiring Databricks CLI v1.8.0 or later. New capabilities include Docker credential helpers for Databricks Artifact Registry, AI Gateway service support in bundles, file-change job run triggers, and bundle improvements for resource reference handling, deployment reporting, and state management.
This release fixes an SSH transfer regression from v1.16.0 that broke transfers larger than 1 MiB through the tunnel layer. The tunnel resume layer was reverted to restore proper functionality.
SSH sessions now automatically reattach and replay missed bytes when the tunnel connection drops temporarily, preventing session interruptions and eliminating manual reconnect workarounds. The CLI adds PyDABs secrets support, JSON output for aitools install with error categorization for CI automation, and displays bundle sync progress by default.
Databricks Asset Bundles introduces PyDABs catalog support, updates default templates to Databricks Runtime 16.4, and adds validation checks before completing migrations to the direct engine. The release also fixes several direct deployment engine update bugs and improves Python fallback handling during local environment setup.