v1.11.5
Summary
Fixed a bug where multiple foreign keys from one table were lost during incremental runs. Fixed materialized view updates to use drop and recreate when partition_by configuration changes.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/dbt-databricks
Metric views now replace existing tables or views using a backup-and-create strategy rather than direct create-or-replace syntax. Lazily formatted Databricks SQL log records are also now properly interpolated when mirrored into dbt logs.
v1.12.4 fixes three bugs: case-insensitive SQL credential redaction, composite unique key deletion on DBR <17.1, and quote escaping in relation comments. databricks-sql-connector is updated to 4.4.0 to fix three CVEs including TLS hostname bypass and DoS vulnerabilities.
Adds catalog_database in v2 catalogs.yml to route Unity catalog models to independent physical catalogs (requires dbt-core 1.12+). Resolves bugs affecting Iceberg Python models, dbt clone operations, non-ASCII column incremental strategies, streaming table tags, and governed tag column drops.
Introduces catalogs.yml v2 support, a skip_optimize config for opting out of post-materialization OPTIMIZE, and Rust kernel backend for SQL warehouses. Fixes numerous incremental model bugs around constraints and tags, but now requires --full-refresh to apply changes to primary/foreign key expressions.
1.12.1
dbt-databricks 1.12.1 exposes job run IDs in adapter responses for correlating dbt runs with Databricks workflow executions and adds support for SPOG account-level vanity URLs. The release fixes critical issues including Iceberg incremental models losing clustering, column constraint enforcement now gated to require explicit contract.enforced: true, and various SQL warehouse compatibility problems.
v1.12.0 adds metric views, row filters, and Python UDFs as new materializations, along with SCHEDULE EVERY and TRIGGER ON UPDATE refresh modes for materialized views and streaming tables. databricks_tags now merge additively across hierarchy levels instead of child replacing parent (breaking change), and the release includes fixes for metric view validation, pydantic v1 compatibility, and streaming table refresh scheduling.