dbt Product Spotlight: Unit tests

You asked, we listened. Unit testing is now live in dbt. And by "we" I really do mean the "royal we". Thank you to everyone in the dbt community who helped make unit testing a reality. 🧡 While dbt users have long been able to build assertions (or tests) about their dbt models (for example: is not null, is unique, etc.), unit tests allow you to validate the behavior of model logic before the model is materialized with real data. Not just "Will this model build?" but "Will it build what I expect it to build?" If a unit test fails, the model won’t build. This saves you from unnecessary data platform spend while improving data product reliability and mitigating the risk of introducing breaking changes into your pipeline.

Lean on unit tests in dbt to:
-Save costs: Validate logic before transforming a full production dataset.
-Improve code reliability: Reduce risk of breaking changes in production.
-Collaborate at scale: Create stable and reliable interfaces for cross-team collaboration.