Upgrade dbt version in Cloud

In dbt, both jobs and environments are configured to use a specific version of dbt Core. The version can be upgraded at any time.

Environments

Navigate to the settings page of an environment, then click Edit. Click the dbt version dropdown bar and make your selection. You can select a release track to receive ongoing updates (recommended), or a legacy version of dbt Core. Be sure to save your changes before navigating away.

Example environment settings in dbt

Release Tracks

Starting in 2024, your project will be upgraded automatically on a cadence that you choose

The Latest track ensures you have up-to-date dbt functionality, and early access to new features of the dbt framework. The Compatible and Extended tracks are designed for customers who need a less-frequent release cadence, the ability to test new dbt releases before they go live in production, and/or ongoing compatibility with the latest open source releases of dbt Core.

As a best practice, dbt Labs recommends that you test the upgrade in development first; use the Override dbt version setting to test your project on the latest dbt version before upgrading your deployment environments and the default development environment for all your colleagues.

To upgrade an environment in the dbt Admin API or Terraform, set dbt_version to the name of your release track:

• latest (formerly called versionless; the old name is still supported)
• compatible (available to Starter, Enterprise, Enterprise+ plans)
• extended (available to all Enterprise plans)

Override dbt version

Configure your project to use a different dbt version than what's configured in your development environment. This override only affects your user account, no one else's. Use this to safely test new dbt features before upgrading the dbt version for your projects.

1. Click your account name from the left side panel and select Account settings.
2. Choose Credentials from the sidebar and select a project. This opens a side panel.
3. In the side panel, click Edit and scroll to the User development settings section.
4. Choose a version from the dbt version dropdown and click Save.

An example of overriding the configured version to "Latest" release track for the selected project:

Example of overriding the dbt version on your user account

5. (Optional) Verify that dbt will use your override setting to build the project by invoking a dbt build command in the Studio IDE's command bar. Expand the System Logs section and find the output's first line. It should begin with Running with dbt= and list the version dbt is using.
   
   For users on Release tracks, the output will display Running dbt... instead of a specific version, reflecting the flexibility and continuous automatic updates provided by the release track functionality.

Jobs

Each job in dbt can be configured to inherit parameters from the environment it belongs to.

Settings of a dbt job

The example job seen in the screenshot above belongs to the environment "Prod". It inherits the dbt version of its environment as shown by the Inherited from ENVIRONMENT_NAME (DBT_VERSION) selection. You may also manually override the dbt version of a specific job to be any of the current Core releases supported by Cloud by selecting another option from the dropdown.

Supported versions

dbt Labs has always encouraged users to upgrade dbt Core versions whenever a new minor version is released. We released our first major version of dbt - dbt 1.0 - in December 2021. Alongside this release, we updated our policy on which versions of dbt Core we will support in the dbt platform.

Starting with v1.0, all subsequent minor versions are available in dbt. Versions are actively supported, with patches and bug fixes, for 1 year after their initial release. At the end of the 1-year window, we encourage all users to upgrade to a newer version for better ongoing maintenance and support.

We provide different support levels for different versions, which may include new features, bug fixes, or security patches:

• Active — We will patch regressions, new bugs, and include fixes for older bugs / quality-of-life improvements. We implement these changes when we have high confidence that they're narrowly scoped and won't cause unintended side effects.
• Critical — Newer minor versions transition the previous minor version into "Critical Support" with limited "security" releases for critical security and installation fixes.
• End of Life — Minor versions that have reached EOL no longer receive new patch releases.
• Deprecated — dbt Core versions older than v1.0 are no longer maintained by dbt Labs, nor supported in dbt platform.

We'll continue to update the following release table so that users know when we plan to stop supporting different versions of Core in dbt.

Latest releases

dbt Core	Initial release	Support level and end date
v1.9	Dec 9, 2024	Active Support — Dec 8, 2025
v1.8	May 9, 2024	End of Life ⚠️
v1.7	Nov 2, 2023	End of Life ⚠️
v1.6	Jul 31, 2023	End of Life ⚠️
v1.5	Apr 27, 2023	End of Life ⚠️
v1.4	Jan 25, 2023	End of Life ⚠️
v1.3	Oct 12, 2022	End of Life ⚠️
v1.2	Jul 26, 2022	Deprecated ⛔️
v1.1	Apr 28, 2022	Deprecated ⛔️
v1.0	Dec 3, 2021	Deprecated ⛔️
v0.X ⛔️	(Various dates)	Deprecated ⛔️

All functionality in dbt Core since the v1.7 release is available in dbt release tracks, which provide automated upgrades at a cadence appropriate for your team.

¹ Release tracks are required for the Developer and Starter plans on dbt. Accounts using older dbt versions will be migrated to the "Latest" release track.

For customers of dbt: dbt Labs strongly recommends migrating environments on older and unsupported versions to release tracks or a supported version. In 2025, dbt Labs will remove the oldest dbt Core versions from availability in dbt platform, starting with v1.0 -- v1.2.

Starting with v1.0, dbt will ensure that you're always using the latest compatible patch release of dbt-core and plugins, including all the latest fixes. You may also choose to try prereleases of those patch releases before they are generally available.

For more on version support and future releases, see Understanding dbt Core versions.

dbt Fusion engine

dbt Labs has introduced the new dbt Fusion engine, a ground-up rebuild of dbt. This is currently in beta on the dbt platform. Eligible customers can update environments to Fusion using the same workflows as v1.x, but there are a few things to keep in mind:

• To gain access to the Fusion Latest release track, you must reach out to your dbt Labs account team to request it. Week by week we'll expand the beta cohort based on project eligibility, including Starter plans. Once we transition from Beta to Preview, all users will see it as an option for their environments, projects, jobs, etc.

To increase the compatibility of your project, update all jobs and environments to the Latest release track and follow our upgrade guide.

• There are some significant changes, these can also be found in the upgrade guide.

• Currently, the only supported adapter is Snowflake. More adapter support coming soon!

• When you change your development environment(s) to Fusion Latest, every user will have to restart the IDE.

  Upgrade to the Fusion engine in your environment settings.

Need help upgrading?

If you want more advice on how to upgrade your dbt projects, check out our migration guides and our upgrading Q&A page.

Testing your changes before upgrading

Once you know what code changes you'll need to make, you can start implementing them. We recommend you:

• Create a separate dbt project, "Upgrade project", to test your changes before making them live in your main dbt project.
• In your "Upgrade project", connect to the same repository you use for your production project.
• Set the development environment settings to run the latest version of dbt Core.
• Check out a branch dbt-version-upgrade, make the appropriate updates to your project, and verify your dbt project compiles and runs with the new version in the Studio IDE.
  • If upgrading directly to the latest version results in too many issues, try testing your project iteratively on successive minor versions. There are years of development and a few breaking changes between distant versions of dbt Core (for example, 1.0 --> 1.10). The likelihood of experiencing problems upgrading between successive minor versions is much lower, which is why upgrading regularly is recommended.
• Once you have your project compiling and running on the latest version of dbt in the development environment for your dbt-version-upgrade branch, try replicating one of your production jobs to run off your branch's code.
• You can do this by creating a new deployment environment for testing, setting the custom branch to 'ON' and referencing your dbt-version-upgrade branch. You'll also need to set the dbt version in this environment to the latest dbt Core version.

Setting your testing environment

• Then add a job to the new testing environment that replicates one of the production jobs your team relies on.
  • If that job runs smoothly, you should be all set to merge your branch into main.
  • Then change your development and deployment environments in your main dbt project to run off the newest version of dbt Core.