How we're making sure you can confidently go "Versionless" in dbt Cloud

As long as dbt Cloud has existed, it has required users to select a version of dbt Core to use under the hood in their jobs and environments. This made sense in the earliest days, when dbt Core minor versions often included breaking changes. It provided a clear way for everyone to know which version of the underlying runtime they were getting.

However, this came at a cost. While bumping a project's dbt version appeared as simple as selecting from a dropdown, there was real effort required to test the compatibility of the new version against existing projects, package dependencies, and adapters. On the other hand, putting this off meant foregoing access to new features and bug fixes in dbt.

But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: "Versionless."

For customers, this means less maintenance overhead, faster access to bug fixes and features, and more time to focus on what matters most: building trusted data products. This will be our stable foundation for improvement and innovation in dbt Cloud.

But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using "Versionless" is a stable, reliable experience for the thousands of customers who rely daily on dbt Cloud.

How we safely deploy dbt upgrades to Cloud

We've put in place a rigorous, best-in-class suite of tests and control mechanisms to ensure that all changes to dbt under the hood are fully vetted before they're deployed to customers of dbt Cloud.

This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected "Versionless" while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers.

We're very confident in the robustness of this process**. We also know that we'll need to continue building trust with time.** We're sharing details about this work in the spirit of transparency and to build that trust.

Any new change to dbt-core and adapters goes through the following steps before it's available to customers in dbt Cloud:

Testing and deploy pipeline

Step 1: Unit & functional tests in dbt Core + adapters

First up is a battery of thousands of tests that we run dozens of times per day. No change, in either dbt-core or in the data platform adapters supported by dbt Cloud, is merged until it has passed this full suite of tests.

Here, unit tests test internal components in isolation from one another, and functional tests represent edge cases in expected behavior under known conditions.

For adapters, tests also ensure that the full matrix of data platform features continue to work as expected: BigQuery partitioning + incremental strategies, Snowflake data types + model contracts, Redshift sort keys — so on and so forth.

Step 2: Smoke testing

Next, we create a Docker image with the latest dbt changes installed alongside each adapter supported in dbt Cloud. We run an additional suite of end-to-end tests on this image across a matrix of supported adapters, test projects that represent real-world complexity, popular third-party packages, and typical dbt user workflows. In doing so, this phase of testing also ensures that the latest version of dbt does not break compatibility with frequently relied-upon dbt packages.

This breadth of testing provides early detection of any regressions that might have been introduced by our changes to dbt-core, changes by adapter maintainers, or any of their dependencies and drivers — using the exact installed versions that would be deployed to dbt Cloud. Crucially, this helps safeguard us from breaking changes in third-party software.

Step 3: dbt Cloud service tests

Before the new image version goes live, we ensure that all dbt changes are cross-compatible with every dbt Cloud service that depends on Core functionality, including areas such as the Cloud IDE, the Cloud CLI, scheduled job runs, CI, and connection testing.

For each dbt Cloud service, we run a testing suite that consists of:

• Unit and integration specific tests to behaviour of each dbt Cloud service
• End-to-end headless browser testing for our UI-heavier applications
• Compatibility for each adapter with that service

This step provides further depth in testing the interplay between dbt Core and dbt Cloud application-specific functionality, covering cases such as linting SQL that has an ephemeral reference, or resolving cross-project refs across multi-project "dbt Mesh" deployments.

Step 4: Canary deployment

Once all the aforementioned tests have passed, we roll out the latest deployment to a small subset (5%) of accounts, including our own Internal Analytics project.

These "canary" deployments are continually monitored against a set of precise observability metrics. Metrics we monitor include overall job error and cancellation rates to ensure they don't deviate from our expectations relative to a stable baseline. Any anomaly immediately alerts us, and we can shut off the canary in a matter of seconds, keeping all accounts on the last stable version.

Step 5: Phased rollout

Once the canary deployment has been proven to run stably for at least 24 hours, we mark it as eligible for all accounts to upgrade in their next scheduled deployment of dbt Cloud.

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Even with a robust testing, deployment, and monitoring system in place, it will never be impossible for a breaking change to make it through — just as in any other SaaS application.

If this does happen, we commit to identifying and rolling back any breaking changes as quickly as possible. Under the new testing and deployment model in dbt Cloud, we are able to roll back erroneous releases in less than an hour.

All incidents are retrospected to make sure we not only identify and fix the root cause(s), but also promptly put in place testing, automation, and quality gates to ensure that the same problem never happens again.

The outcome of this process is that, when you select "Versionless" in dbt Cloud, the time between an improvement being made to dbt Core and you safely getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade.

We’re pleased to say that since the beta launch of “Versionless” in dbt Cloud in March, we have not had any functional regressions reach customers, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future.

Stability as a feature

A rigorous testing pipeline in dbt Cloud is crucial, but real ongoing stability required some deeper changes in the dbt framework itself. We take our responsibility as the maintainers of dbt Core seriously, as well the open-source ecosystem around it.

We've taken a longer release cycle for the upcoming release of dbt Core v1.8 to revisit some of the "do later" design choices we made in the past — specifically around adapter compatibility, behaviour change management, and metadata artifacts.

Decoupling the adapter interface

The adapter interface — i.e. how dbt Core actually connects to a third-party data platform — has historically been somewhat of a pain point. Adapter maintainers have often been required to make reactive changes when there's been an update to dbt Core.

To solve that, we've released a new set of interfaces that are entirely independent of the dbt-core library: dbt-adapters==1.0.0. From now on, any changes to dbt-adapters will be backward and forward-compatible. This also decouples adapter maintenance from the regular release cadence of dbt Core — meaning maintainers get full control over when they ship implementations of new adapter-powered features.

Note that adapters running in dbt Cloud must be migrated to the new decoupled architecture as a baseline in order to support the new "Versionless" option.

Managing behavior changes: stability as a feature

We all want the benefits of a stable, actively maintained product. Occasionally the dbt Labs team sees the opportunity for a change to default behaviour that we believe is more sensible, more secure, more helpful — just better in some way — but which would come as a change to users who have grown accustomed to the existing behaviour.

To accommodate both groups in these scenarios, we've extended dbt to support project-level behavior flags. These can be used to opt into or opt out of changes to default behavior. From now on, backward-incompatible changes to dbt functionality will be implemented behind a flag with a default value that preserves the legacy behavior. After a few months, the new behavior will become the default — but only after some proactive communication with customers and external package maintainers.

The same behavior change flags will naturally extend to dbt packages, which are fundamentally just dbt projects. This allows package maintainers to ensure that behavior doesn't change unexpectedly as a result of changes to dbt Core. For more details, check out our user documentation on legacy behaviors, as well as our contributor documentation for introducing behavior changes safely.

Stability for metadata artifacts

Lastly, we’ve revisited our process around artifact interfaces. These are the workhorses of many integrations in the dbt ecosystem: those maintained by dbt Labs, by third-party vendors, or just homegrown at a particular organization. While these schemas have been versioned and well-defined since dbt Core v1.0, they have changed in many of the minor releases since.

We’ve now formalized our development best practices to strongly prefer minor schema evolutions over major breaking changes. We’ve also put checks in place to ensure we’re not unintentionally introducing breaking changes to artifacts, thus avoiding disruption to integrations across the ecosystem.

Our commitment

In conclusion, we’re putting a lot of new muscle behind our commitments to dbt Cloud customers, the dbt Community, and the broader ecosystem:

• Continuous updates: "Versionless" dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead.
• A rigorous new testing and deployment process: Our new testing pipeline ensures that every update is carefully vetted against documented interfaces, Cloud-supported adapters, and popular packages before it reaches you. This process minimizes the risk of regressions — and has now been successful at entirely preventing them for hundreds of customers over multiple months.
• A commitment to stability: We’ve reworked our approaches to adapter interfaces, behaviour change management, and metadata artifacts to give you more stability and control.

As we continue to enhance dbt Cloud, our commitment remains firm: to provide a stable, dependable platform that allows our users to spend less time on maintenance overhead and focus on creating value.