v0.171.0
Summary
The SDK adds job triggers for scheduling, API source connectors for pipelines, secret value retrieval in requests, new GpuXlarge8 workload types, Netsuite connection support, and token forwarding for apps. Breaking changes remove browse-related fields from model and MCP service APIs and make several user and service principal fields required.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/databricks-sdk-go
The SDK adds new methods for managing groups, users, service principals, and workspace assignments at both account and workspace levels, providing expanded IAM capabilities. The NewCluster field in JobCluster is now optional, a breaking change that affects existing cluster configuration code.
The Go SDK adds fields for granular billing control: `PrincipalOverrides`, `ScopeType`, and `ResourceType` for alerts and budgets, plus a new `BlockUsage` action type for alert configuration. The catalog API now supports grant management for policies via a new `Grant` field and `PolicyTypeGrant` enum type.
Breaking changes include removal of direct AWS and Azure credential fields from model provider configurations and State field type changes in bundle deployment operations. New features add KinesisStreamConfig support for ML streaming sources, a Mode field for pipeline updates, and Pi coding agent attribution in the User-Agent header.
The SDK adds new fields to job triggers (PauseStatus), workspace objects (DirectoryInfo), and IAM entities (GroupId, ServicePrincipalId, UserId) for enhanced API access. A breaking change removes the InternalId field from IAM Group, ServicePrincipal, and User types, requiring code updates for any existing consumers.
The Go SDK adds AwsAccessKey authentication for Amazon Bedrock model providers and EntraServicePrincipal authentication for Azure OpenAI and Microsoft Foundry providers. These new configuration options enable additional credential management methods when integrating external AI models through Databricks.