Summary
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.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
Topics
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Databricks Asset Bundles now correctly handles app updates during bundle deployment when an active deployment already exists. It also fixes reference resolution and deployment failures for resource keys starting with an underscore.
Bundles automatically migrate from Terraform state to the direct engine on clean deploys (opt-out via engine: terraform), and SSH sessions stay connected during idle periods with automatic keepalives every 30 seconds. Direct engine bundle operations now correctly handle removed configuration fields, support cluster policies resources, and include a DATABRICKS_BUNDLE_RESOURCE_MAX_WAIT timeout setting.
Databricks Asset Bundles now report detailed resource action summaries and file sync counts during deploy and destroy operations, alongside fixes for workspace Git folder metadata and state migration. The CLI now enables FIPS 140-3 compliance by default for TLS connections, adds Goose support to aitools, and automatically resolves conflicting databricks-connect pins in local environment setups.
Bump github.com/databricks/databricks-sdk-go from v0.166.0 to v0.170.0 (#6251).
The CLI adds databricks environments setup-local to provision matched Python environments for Databricks compute targets and extends aitools install to support Gemini CLI and Pi. Bundles fix the ignored bundle.deployment.lock.force setting, add pipeline cascade_on_destroy control, improve experimental job_runs with idempotency tokens and completion waiting, and add UC secrets resource support.
The Terraform deployment engine is now deprecated; migrate to the direct deployment engine for continued support. AI runtime tasks now automatically package local directories into tarballs during deployment, and the direct engine fixes several convergence issues with empty grants, webhook notification ordering, pipeline configuration, and vector search index creation.