dbt-databricks

The SDK now caches OIDC tokens and makes `WorkspaceClient.dbutils` lazy, preventing crashes on Spark Connect clusters and improving performance. A breaking change makes the `resource_id` field optional for `bundledeployments.Operation`, and `type_overrides` fields were added to `SyncedTableSpec` for database and postgres services.

1.12.1

This release exposes Databricks Jobs IDs in dbt's adapter response for better run correlation and adds support for Databricks SPOG vanity URLs. It also fixes issues with streaming tables, materialized views, column-level constraints, and managed Iceberg incremental models.

This release adds support for metric views, row filters, Python UDFs, and key-only Databricks tags. It also includes a breaking change where Databricks tags now merge additively across hierarchy levels.

This release adds the dbt invocation ID to default query comments and enforces the 255-character identifier length limit for Databricks relations. It also resolves several issues, including spurious warnings for MicrobatchConcurrency and insert_overwrite, and improves handling of materialized views and Iceberg tables.

Using dbt with Databricks: Architecture decisions that determine success

Databricks users who skip dbt incur compounding costs. A solution architect explains key architecture decisions and when to act to ensure success.

This release adds support for Notebook-scoped packages when submitting commands or running notebook jobs. It also includes fixes for workflow job creation, preventing duplicate aliases in empty mode, and enabling insert-by-name for microbatch and replace_where strategies.

This release enables concurrent microbatch execution and adds an optimize() call to snapshot materialization. Fixes include quoting catalog names, adding a cluster clause to streaming table alter SQL, and applying column-level tags for V1 table materialization.

This release introduces new row filter functionality and support for metric views.

This release fixes an issue where multiple foreign keys between tables were not retained after an incremental run. It also resolves a bug where changes to materialized view partition_by clauses failed to apply correctly.

This release updates the dbt-core pin for the 1.10.latest version. No user-facing features, fixes, or breaking changes are included.

This release updates internal dbt-common and dbt-adapter dependencies for the 1.10.x series. No user-facing features, fixes, or breaking changes are included.

This release updates the dbt-core dependency pin. No user-facing features, fixes, or breaking changes are included.

* feat: Add query-id to SQLQueryStatus by @colin-rogers-dbt in https://github.com/databricks/dbt-databricks/pull/1280

This release updates the dbt-core upper bound, enabling compatibility with dbt-core version 1.10.16. This allows Databricks practitioners to use dbt-databricks with the latest 1.10.x dbt-core releases.

This release fixes an issue where default query tag values were not properly escaped or truncated. Databricks practitioners will now see correct handling of these values in their dbt projects.