v0.154.0
Summary
The Java SDK now supports purging feature entities via a new method and adds deny-based policy controls in the catalog service. ML feature ingestion and materialized features now support budget policies and tags for improved cost management and organization.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/databricks-sdk-java
Breaking changes remove rate limiting fields and enums while adding a new domains service and sandbox command execution method. The ML feature engineering APIs gain new fields for handling data sources, time windows, and scheduling.
This release adds SDK support for AI Functions and Sandboxes, as well as new backfill management methods for Feature Engineering. Breaking changes remove the owner field across multiple catalog model services, along with trafficSplitting from model routing and disabled from inference table configurations.
This release adds group assumption support across OAuth and workload identity federation authentication flows, as well as new API methods for Postgres snapshots, Genie cancellation, and RabbitMQ pipeline ingestion. Breaking changes remove several fields across ML service models and make source schema and table fields optional in pipeline specifications.
This release contains internal test infrastructure improvements. The TestOSUtils.resource method was updated to stage resources to temporary directories when they cannot be modified in place, enabling unit tests to run unchanged under non-Maven build systems while preserving existing Maven behavior.
The SDK now accepts both JSON numbers and decimal strings when deserializing int64 response fields, fixing deserialization compatibility issues. A new enabledTelemetryFeatures field was added to TelemetryConfig for the Serving API.
This release adds git integration support to Databricks Apps with auto-deploy capabilities and enhanced git repository configuration options. New customUdf field for functions and effectiveServerlessComputeId field for pipeline responses enable better resource tracking and custom function capabilities.
