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Writing custom generic data tests dbt ships with not null, unique, relationships, and accepted values generic data tests Dbt ships with four generic data tests built in, and we think you should use them! (these used to be called schema tests, and you'll still see that name in some places.) under the hood, these generic data tests are defined as test blocks (like macros).
Writing a custom generic test in dbt allows you to test your data on your own assumptions Once defined, you can reference the generic test by name throughout your.yml files—define it on models, columns, sources, snapshots, and seeds For a project that we worked on, i have written a test to check a column that has integers that represent a ranking
The project included many data transformations, and thus i wanted to check that in my end table the rank values made sense (i.e
No duplicates or unexpected values) If you want to add dbt tests but don’t know how to get started or your team is overwhelmed maintaining dbt tests, i hope you find this guide helpful It covers popular dbt testing packages, schema tests, freshness tests, volume tests, column tests and custom dbt tests. Here's everything you need to know about dbt tests, including what they are, how to write them, and best practices for implementing them in your data workflow.
A basic generic test in our example below, we're going to add a test that essential checks that 2 tables are equivalent via the sql minus operator Add 2 simple models that we'll use to simulate an error: This guide will help you understand how to create and use custom tests to ensure the quality and reliability of your data transformations. A look into generic tests and how to create custom singular tests for robust data validation.
The test query is defined in a special test block (like a macro)
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