Summary
This release introduces a new bundle package and workspace-level service for managing Databricks Asset Bundles. It also adds an MtlsConfig field to ml.AuthConfig for mTLS authentication configurations.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
Topics
More from databricks/databricks-sdk-go
Release introduces three new workspace services (AI Functions, Domains, Sandbox), extends Feature Engineering with new backfill and operation management methods, and adds AWS Secrets Manager and Azure Key Vault connection support. Multiple breaking changes remove fields from model serving and catalog configurations that may require code updates.
This release adds support for Postgres snapshot operations, RabbitMQ pipeline ingestion, external IAM entity lookups, and H100 GPU compute. It also introduces breaking changes by removing several ML service fields and making source schemas and tables optional in pipeline specifications.
The SDK now supports group role assumption in OAuth M2M and workload identity federation authentication. New safeguards prevent other credential strategies, including Databricks CLI authentication, from silently accessing normal-access credentials.
Add EnabledTelemetryFeatures field for serving.TelemetryConfig.
Fixes OAuth token cache conflicts in multi-process deployments and Spark runtime version handling, with breaking changes requiring code updates for pagination in IAM list operations. Introduces git-based deployment for apps and custom UDF support in ML functions.
Go version requirement raised to 1.25, and IAM list methods now include pagination with breaking signature changes, while CreateOperation is removed from BundleDeployments. Fixes concurrent OAuth token cache handling and Spark runtime selection for major-only versions.