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description: Check if PR title follows conventional-commits format
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permissions:
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pull-requests: write
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on:
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pull_request:
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types:
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- opened # A pull request was created
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- edited # The title or body of a pull request was edited.
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- synchronize # A pull request's head branch was updated. For example, the head branch was updated from the base branch or new commits were pushed to the head branch.
Copy file name to clipboardExpand all lines: CHANGELOG.md
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## dbt-databricks 1.10.10 (TBD)
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## dbt-databricks 1.10.11 (TBD)
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## dbt-databricks 1.10.10 (August 20, 2025)
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### Fixes
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- Gate column comment syntax on DBR version for better compatibility ([1151](https://github.com/databricks/dbt-databricks/pull/1151))
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### Documentation
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- Update Databricks Job documentation to match current terminology ([1145](https://github.com/databricks/dbt-databricks/pull/1145))
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## dbt-databricks 1.10.9 (August 7, 2025)
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### Features
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- Support column tags for views using `ALTER TABLE`
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### Under the hood
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- Revert `REPLACE USING` syntax being used for insert overwrite ([1025](https://github.com/databricks/dbt-databricks/issues/1025))
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## dbt-databricks 1.10.8 (August 4, 2025)
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### Features
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- Support insert_overwrite incremental strategy for SQL warehouses ([1025](https://github.com/databricks/dbt-databricks/issues/1025))
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### Fixes
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- Add fallback logic for known error types for `DESCRIBE TABLE EXTENDED .. AS JSON` for better reliability ([1128](https://github.com/databricks/dbt-databricks/issues/1128))
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- Fix no-op logic for views that is causing some incremental materializations to be skipped ([1122](https://github.com/databricks/dbt-databricks/issues/1122))
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# Running a dbt project as a job in Databricks Workflows
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# Running a dbt project as a job in Databricks Jobs
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Databricks Workflows is a highly-reliable, managed orchestrator that lets you author and schedule DAGs of notebooks, Python scripts as well as dbt projects as production jobs.
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Databricks Lakeflow Jobs is a highly-reliable, managed orchestrator that lets you author and schedule DAGs of notebooks, Python scripts as well as dbt projects as production jobs.
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In this guide, you will learn how to update an existing dbt project to run as a job, retrieve dbt run artifacts using the Jobs API and debug common issues.
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- Install and configure the [Databricks CLI](https://docs.databricks.com/dev-tools/cli/index.html)
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- Install [jq](https://stedolan.github.io/jq/download/), a popular open source tool for parsing JSON from the command line
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Note: previously dbt tasks on Databricks Workflows could target jobs clusters for compute.
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Note: previously dbt tasks on Databricks Jobs could target jobs clusters for compute.
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That is [no longer supported](https://docs.databricks.com/en/workflows/jobs/how-to/use-dbt-in-workflows.html#advanced-run-dbt-with-a-custom-profile).
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Job clusters can only be used for running the dbt-cli.
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