v0.103.0

This release adds new API fields across multiple Databricks services, including support for Unity Catalog image paths in AI runtime tasks, feature view sources in ML data sources, budget policies and tags in ML publishing, and a new GPU_8X_B300 compute type. These additions enable Java SDK users to access recently added Databricks platform capabilities for jobs, ML workflows, pipelines, and workspace settings management.

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.

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.