v0.132.0
Summary
The SDK adds git integration and auto-deploy capabilities for Databricks Apps, along with custom UDF support for ML functions. Pipeline responses now include effective serverless compute IDs for better visibility into resource allocation.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/databricks-sdk-py
The action_type and status fields in the bundledeployments.Operation model are no longer required. Additionally, the create_operation method has been removed from the workspace-level bundle_deployments service.
The SDK now supports LinkedIn Ads and Marketo as pipeline connectors and expands PostgreSQL synced table support with halfvec and varchar types. The list_workspace_assignments methods now require pagination handling, which is a breaking change for existing code calling these APIs.
This release adds pagination to four IAM methods in AccountIamV2API and WorkspaceIamV2API (list_direct_group_members and list_workspace_assignment_details), which is a breaking change requiring code updates if you use these methods. Several new fields are added across services including agent_type for Genie conversations, group_id for direct group members, effective_serverless_compute_id for job runs, and pipeline_channel for Postgres pipelines.
The SDK adds comprehensive IAM v2 methods for account and workspace-level management of users, groups, service principals, and workspace assignments. The `new_cluster` field for `JobCluster` is now optional, which is a breaking change.
The SDK now supports job triggers and MLflow artifact locations in the Jobs API, enabling more flexible job scheduling and artifact management. New connector configuration options are available for Pipelines, and GCP endpoint settings have been expanded.
The SDK removes the include_browse field from catalog service requests and browse_only field from service objects, which is a breaking change requiring code updates for affected users. New features include forward_user_access_token for applications, docker_image_url for AI runtime jobs, gpu_xlarge_8 GPU workload size, and required IAM fields for users and service principals.