What are custom tests in dbt?

Custom tests in dbt refer to user-defined checks for data integrity. These tests allow analysts and engineers to create specific assertions to ensure that the data meets certain conditions or business rules. By defining custom tests, users can validate data consistency, accuracy, and completeness tailored to their specific requirements, which enhances trust in the data being analyzed.

For instance, a custom test might check that no values in a particular column are null, or that certain relationships between data points hold true across datasets. This flexibility enables users to implement checks that can capture unique scenarios relevant to their business logic or data model.

The other options do not accurately capture the essence of custom tests. Pre-built tests may serve as a foundation but do not offer the customization feature that allows users to tailor their checks. Tests that run automatically could imply a lack of user-defined parameters, which is contrary to the nature of custom tests that rely on specific, user-defined criteria. Lastly, comparative tests typically involve analyzing relationships between models, which is not the primary focus of what custom tests represent in dbt.