dbt-databricks

This release adds support for metric views, row filters, Python UDFs, and key-only Databricks tags. A breaking change alters how `databricks_tags` merge 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 table properties.

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, duplicate aliases in empty mode, and insert-by-name for microbatch and replace_where strategies.

This release enables concurrent microbatch execution and adds an optimize() call to snapshot materialization. It also fixes issues with quoted catalog names, streaming table alter SQL, missing optimize calls for table v2, column-level tags for V1 tables, and constraint enforcement.

This release introduces 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, now using a DROP and CREATE strategy.

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.

This release adds a query-id to SQLQueryStatus for improved tracking. It also fixes an issue where hard_deletes incorrectly invalidated active records in snapshots and addresses serverless Python model environment version configuration.

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.