Upgrade to "Versionless" in dbt Cloud
Since May 2024, new capabilities in dbt are delivered continuously to dbt Cloud. We call this "versionless dbt," because your projects and environments are upgraded automatically.
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 "Versionless," you can get all the functionality in the latest features before they're in dbt Core — and more! — along with access to the new features and fixes as soon as they’re released.
If you regularly develop your dbt project in dbt Cloud and this is your first time trying “Versionless,” dbt Labs recommends that you try upgrading your project in a development environment. Override your dbt version in development. Then, launch the IDE or Cloud 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 Cloud 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.
The following is our guidance on some important considerations regarding dbt projects as part of the upgrade.
To learn more about how dbt Labs deploys stable dbt upgrades in a safe manner to dbt Cloud, we recommend that you read our blog post How we're making sure you can confidently go "Versionless" in dbt Cloud for details.
If you're running dbt version 1.6 or older, please know that your version of dbt Core 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 Core v1.7 for dbt Cloud Enterprise customers.
If you're running dbt version v1.6 or older, please know that your version of dbt Core 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 Core v1.7 for dbt Cloud Enterprise customers while we work through the migration with those customers to automatic upgrades. In the meantime, this means that v1.7 will continue to be accessible in dbt Cloud for Enteprise customers, jobs and environments on v1.7 for those customers will not be automatically migrated to "Versionless," and dbt Labs will continue to fix critical bugs and security issues.
dbt Cloud accounts on the Developer and Team plans will be migrated to "Versionless" dbt after November 1, 2024. If you know that your project will not be compatible with the upgrade, for one of the reasons described here, or a different reason in your own testing, you should contact dbt Cloud support to request an extension.
If your account has been migrated to "Versionless," and you are seeing net-new failures in your scheduled dbt jobs, you should also contact dbt Cloud support to request an extension.
If you are upgrading from a very old unsupported version of dbt Core, 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 Cloud 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](https://docs.getdbt.com/docs/dbt-versions/core-upgrade/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-core#7371
You should contact dbt Cloud support to request an extension, during which you will need to make those updates.
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 Cloud support to request an extension, during which you will need to make those updates.
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 “Versionless” and immediately see something that breaks, please contact support and, in the meantime, downgrade back to v1.7.
If you’re already on “Versionless” 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 Cloud). 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.
No. Going forward, customers will access new functionality and ongoing support in dbt Cloud by receiving automatic updates. We believe this is the best way for us to offer a reliable, stable, and secure runtime for dbt, and for you as dbt users to be able to consistently take advantage of new features.
In 2023 (and earlier), customers were expected to manage their own upgrades by selecting dbt Core versions, up to and including dbt Core v1.7, which was released in October 2023. (Way back in 2021, dbt Cloud customers would pick specific patch releases of dbt Core, such as upgrading from v0.21.0 to v0.21.1. We’ve come a long way since then!)
In 2024, we've changed the way that new dbt functionality is made available for dbt Cloud customers. Behavior or breaking changes are gated behind opt-in flags. Users don't need to spend valuable time managing their own upgrades. Currently, it is possible to receive continuous (daily) updates. We are adding other release cadence options for managed customers of dbt Cloud by the end of the year.
Opting into a release cadence with automated upgrades is required for accessing any new functionality that we've released in 2024, and going forward.
We continue to release new minor versions of dbt Core (OSS), including v1.9 which will be available later this year. When we do, it will be a subset of the functionality that's already available to dbt Cloud customers, and always after the functionality has been available in dbt Cloud.
If you have comments or concerns, we’re happy to help. If you’re an existing dbt Cloud customer, you may reach out to your account team or contact support.