dbt v0.14.1 includes a number of bug fixes and improvements to the dbt workflow. These changes include UI improvements for dbt’s console output, updates to the dbt Docs website, and general performance improvements. Check out the release notes for a complete list of changes in this release.
Note: This release includes an important fix for the
check snapshot strategy on Snowflake and BigQuery. See this guide for more information.
Some selected highlights from the Changelog to follow.
Warnings are summarized at the end of dbts runs
Test failures that are configured with a
warning severity will now be displayed in the end-of-run summary. Additionally, a summary count of tests in the
WARN state is shown at the end of the run.
Add support for clustering on Snowflake
Shout out to Bastien Boutonnet for this really well-done feature! Table clustering is a powerful tool for managing costs and query times on particularly large Snowflake tables. In dbt v0.14.1, a list of
clustering keys can be provided as a configuration to a table or incremental model. When configured to cluster tables, dbt will automatically order your tables by the specified clustering keys to reduce automatic clustering costs. For full usage details, check out the documentation.
Configurable BigQuery job priorities
Another user-contributed feature! This one was submitted by Stephen Whitworth. The
priority for the BigQuery jobs that dbt executes can now be configured in your BigQuery profile. The
priority config can be set to one of
interactive. For usage instructions, check out the dbt documentation, and for more information on query priorities, consult the BigQuery docs.
Add support for Panoply Redshift
So we have that going for us. Which is nice.
Thanks to our contributors!
If you’re interested in working on a feature in the dbt backlog, check out the Contributing Guide and drop us a line on Slack! Thanks to the following contributors who submitted PRs for the 0.14.1 release: