Release tracks in dbt platform

Since May 2024, new capabilities in the dbt framework are delivered continuously to dbt. Your projects and environments are upgraded automatically on a cadence that you choose, depending on your dbt plan.

Previously, customers would pin to a minor version of dbt v1, and receive only patch updates during that specific version's active support period. Release tracks ensure that your project stays up-to-date with the modern capabilities of dbt and recent versions of dbt v1.

This will require you to make one final update to your current jobs and environments. When that's done, you'll never have to think about managing, coordinating, or upgrading dbt versions again.

By moving your environments and jobs to release tracks you can get all the functionality in dbt as soon as it's ready. On the v1 Latest release track, this includes access to features before they're available in final releases of dbt v1 OSS.

Which release tracks are available?

dbt v2 release tracks

The dbt platform has four dbt v2 release tracks available to give you control over stability vs. velocity.

v2 Stable is the default for all new dbt v2-powered projects.

Release track	Cadence	Description	Plan availability	API value
v2 Nightly	Nightly	The latest nightly build. Includes early access to new features.	All plans	fusion-nightly
v2 Stable (default)	Weekly	A weekly release that balances stability and feature access.	All plans	fusion-stable
v2 Extended	Monthly	The previous month's final v2 Stable release. Designed for those who want maximum stability and additional testing time.	Enterprise, Enterprise+	fusion-extended
v2 Fallback	Monthly	The previous month's v2 Extended release. Emergency rollback option for account admins.	Enterprise+	fusion-fallback

dbt v1 release tracks

Release track	Description	Plan availability	API value
v1 Latest	Provides a continuous release of the latest functionality in the dbt platform . Includes early access to new features of the dbt framework before they're available in dbt v1.	All plans	latest
v1 Compatible	Provides a monthly release aligned with the most recent open source versions of dbt v1 and adapters, plus functionality exclusively available in the dbt platform. See v1 Compatible track changelog for more information.	Starter, Enterprise, Enterprise+	compatible
v1 Extended	The previous month's v1 Compatible release.	Enterprise, Enterprise+	extended
v1 Fallback	The previous month's v1 Extended release.	Enterprise+	fallback

To configure an environment in the dbt Admin API or Terraform to use a release track, set dbt_version to the release track name:

• fusion-nightly
• fusion-stable (formerly latest-fusion)
• fusion-extended
• fusion-fallback
• latest
• compatible
• extended

Which release track should I choose?

Choosing a dbt v2 release track

Choose v2 Stable (the default for new projects) for a weekly release cadence that balances stability and feature access. This is the recommended starting point for most customers migrating to dbt v2.

Choose v2 Nightly if you want the latest features and are comfortable with a daily release cadence. This track ideal for teams prioritizing the most recent features.

Choose v2 Extended if your team needs maximum stability and more time to test before updates roll out. This track generally lags v2 Stable by one month and is available for Enterprise and Enterprise+ accounts.

Choose v2 Fallback as an emergency measure only. This is a temporary rollback option for Enterprise+ accounts, not for ongoing use.

Account admins can set the org-wide default release track for new projects via a toggle in Account Settings. Individual project owners can then override that default per environment.

Choosing a dbt v1 release track

Choose the v1 Latest release track to continuously receive new features, fixes, performance improvements — latest & greatest dbt. This is the default for all customers on dbt.

Choose the v1 Compatible and v1 Extended release tracks if you 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 v1.

Using the v2 Fallback release track

The v2 Fallback release track provides an emergency rollback option for account admins if you suspect a regression in the "v2 Extended" track. This is available only for dbt v2 in Enterprise+ accounts.

1. Go to Account settings
2. Click the Revert to Fallback release track button in the dbt platform interface. This is not accessible through environment settings.
3. Fill in the details in the Revert to Fallback pop-up to confirm and share information with dbt Support.

Switching to v2 Fallback alerts the dbt Support team, who may reach out to help resolve the issue. This track is meant only as a temporary safety option to unblock you and not for ongoing use. Return to "v2 Extended" or "v2 Stable" (dbt v2) / "v1 Compatible" (dbt v1) once the issue is resolved.

v2 Fallback release track button in dbt platform

v2 Fallback release track popup in dbt platform

Common architectures

dbt v2 track architectures

Default — All plans

• Prioritize a weekly release cadence that balances stability and new features
• Leave all environments on the v2 Stable release track (default for new projects at GA)

Velocity-focused — All plans

• Prioritize immediate access to the latest v2 features and fixes
• Configure all environments to use the v2 Nightly release track

Cautious — Enterprise, Enterprise+

• Prioritize maximum stability and additional testing time
• Configure production environments to use v2 Extended (one month behind v2 Stable)
• Configure development environments to use v2 Stable so developers get access to upcoming changes before they reach production

dbt v1 track architectures

Default — Majority of customers on all plans

• Prioritize immediate access to fixes and features
• Leave all environments on the v1 Latest release track (default configuration)

Hybrid — Starter, Enterprise, Enterprise+

• Prioritize ongoing compatibility between dbt and dbt v1 for development & deployment using both products in the same dbt projects
• Configure all environments to use the v1 Compatible release track
• Understand that new features will not be available until they are first released in dbt v1 OSS (several months after the v1 Latest release track)

Cautious — Enterprise, Enterprise+, Business Critical

• Prioritize "bake in" time for new features & fixes
• Configure development & test environments to use the v1 Compatible release track
• Configure pre-production & production environments to use the v1 Extended release track
• Understand that new features will not be available until a month after they are first released in dbt v1 OSS and the v1 Compatible track. Developers (on v1 Compatible) will get access to new features before they can leverage those capabilities in production (on v1 Extended), and must be mindful of the additional delay.

Virtual Private dbt or Single Tenant

• Changes to all release tracks roll out as part of dbt instance upgrades once per week

Upgrading from older versions

How to upgrade

If you regularly develop your dbt project in dbt, and you're still running on a legacy version of dbt v1, dbt Labs recommends that you try upgrading your project in a development environment. Override your dbt version in development. Then, launch the Studio IDE or dbt platform CLI and do your development work as usual. Everything should work as you expect.

If you do see something unexpected or surprising, revert back to the previous version and record the differences you observed. Contact dbt support with your findings for a more detailed investigation.

Next, we recommend that you try upgrading your project’s deployment environment. If your project has a staging deployment environment, upgrade and try working with it for a few days before you proceed with upgrading the production environment.

If your organization has multiple dbt projects, we recommend starting your upgrade with projects that are smaller, newer, or more familiar for your team. That way, if you do encounter any issues, it'll be easier and faster to troubleshoot those before proceeding to upgrade larger or more complex projects.

Considerations

To learn more about how dbt Labs deploys stable dbt upgrades in a safe manner to dbt, we recommend that you read our blog post: How we're making sure you can confidently switch to the "Latest" release track in dbt.

If you're running dbt version 1.6 or older, please know that your version of dbt v1 has reached end-of-life (EOL) and is no longer supported. We strongly recommend that you update to a newer version as soon as reasonably possible.

dbt Labs has extended the critical support period of dbt v1.7 for dbt Enterprise-tier customers to March 2025. At that point, we will be encouraging all customers to select a Release Track for ongoing updates in dbt.

 I'm using an older version of dbt in the dbt platform. What should I do? What happens if I do nothing?

If you're running dbt version v1.6 or older, please know that your version of dbt v1 has reached end-of-life (EOL) and is no longer supported. We strongly recommend that you update to a newer version as soon as reasonably possible.

dbt Labs has extended the "Critical Support" period of dbt v1.7 for dbt Enterprise-tier customers while we work through the migration with those customers to Release Tracks. In the meantime, this means that v1.7 will continue to be accessible in dbt for Enterprise customers, jobs and environments on v1.7 for those customers will not be automatically migrated to "Latest," and dbt Labs will continue to fix critical bugs and security issues.

Starting in October 2024, dbt accounts on the Developer and Starter (formerly Teams) plans have been migrated to release tracks from older dbt v1 versions. If your account was migrated to the v1 Latest release track and you notice new failures in scheduled jobs, please contact dbt support to report the problem or request an extension.

 What are other known issues when upgrading from older v1 versions?

If you are upgrading from a very old unsupported version of dbt v1, you may run into one of these edge cases after the upgrade to a newer version:

• [v1.1] Customers on BigQuery should be aware that dbt sets a default per-model timeout of 5 minutes. You may override this config in your connection details. Older versions of dbt (including v1.0) did not appropriately respect this timeout configuration.
• [v1.3] Customers with non-dbt .py files defined within their project directories, such as models/. Since v1.3, dbt expects these files be valid Python models. The customer needs to move these files out of their models/ directory, or ignore them via .dbtignore
• [v1.5] Customers who have --m in their job definitions, instead of -m or --models. This autocompletion (--m[odels] for --models) has never been officially documented or supported. It was an implicit behavior of argparse (CLI library used in dbt-core v1.0-1.4) that is not supported by click (the CLI library used in dbt-core since v1.5+).
• [v1.5] Empty invalid tests config start raising a validation error](/docs/dbt-versions/dbt-upgrade/Older%20versions/upgrading-to-v1.5). Replace empty tests config with tests: [] or remove it altogether.
• [v1.6] Performance optimization to load_result means you cannot call it on the same query result multiple times. Instead, save it to a local variable once, and reuse that variable (context: dbt-labs/dbt#7371

You should contact dbt support to request an extension, during which you will need to make those updates.

 I see that my account was migrated to v1 Latest. What should I do

For the vast majority of customers, there is no further action needed.

If you see new failures in your scheduled jobs now that they are running on a newer version of dbt, you may need to update your project code to account for one of the edge cases described on this page. You should contact dbt support to request an extension, during which you will need to make those updates.

 What about breaking changes to packages (maintained by dbt Labs or by others)?

When we talk about latest version, we’re referring to the underlying runtime for dbt, not the versions of packages you’re installing. Our continuous release for dbt includes testing against several popular dbt packages. This ensures that updates we make to dbt-core, adapters, or anywhere else are compatible with the code in those packages.

If a new version of a dbt package includes a breaking change (for example, a change to one of the macros in dbt_utils), you don’t have to immediately use the new version. In your packages configuration (in dependencies.yml or packages.yml), you can still specify which versions or version ranges of packages you want dbt to install. If you're not already doing so, we strongly recommend checking package-lock.yml into version control for predictable package installs in deployment environments and a clear change history whenever you install upgrades.

If you upgrade to the v1 Latest release track, and immediately see something that breaks, please contact support and, in the meantime, downgrade back to v1.7.

If you’re already on the v1 Latest release track, and you observe a breaking change (like something worked yesterday, but today it isn't working, or works in a surprising/different way), please contact support immediately. Depending on your contracted support agreement, the dbt Labs team will respond within our SLA time and we would seek to roll back the change and/or roll out a fix (just as we would for any other part of dbt). This is the same whether or not the root cause of the breaking change is in the project code or in the code of a package.

If the package you’ve installed relies on undocumented functionality of dbt, it doesn't have the same guarantees as functionality that we’ve documented and tested. However, we will still do our best to avoid breaking them.

If you have comments or concerns, we’re happy to help. If you’re an existing dbt customer, you may reach out to your account team or contact support.

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