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Upgrading to v1.10

Resources​

What to know before upgrading​

dbt Labs is committed to providing backward compatibility for all versions 1.x. Any behavior changes will be accompanied by a behavior change flag to provide a migration window for existing projects. If you encounter an error upon upgrading, please let us know by opening an issue.

Starting in 2024, dbt Cloud provides the functionality from new versions of dbt Core via release tracks with automatic upgrades. If you have selected the "Latest" release track in dbt Cloud, you already have access to all the features, fixes, and other functionality that is included in dbt Core v1.10! If you have selected the "Compatible" release track, you will have access in the next monthly "Compatible" release after the dbt Core v1.10 final release.

For users of dbt Core, since v1.8, we recommend explicitly installing both dbt-core and dbt-<youradapter>. This may become required for a future version of dbt. For example:

python3 -m pip install dbt-core dbt-snowflake

New and changed features and functionality​

New features and functionality available in dbt Core v1.10

The --sample flag​

Large data sets can slow down dbt build times, making it harder for developers to test new code efficiently. The --sample flag, available for the run and build commands, helps reduce build times and warehouse costs by running dbt in sample mode. It generates filtered refs and sources using time-based sampling, allowing developers to validate outputs without building entire models.

Quick hits​

  • Provide the loaded_at_query field for source freshness to specify custom SQL to generate the maxLoadedAt time stamp on the source (versus the built-in time stamp, which uses the loaded_at_field). You can not define loaded_at_query if the loaded_at_field config is also provided.

Coming soon

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