The dbt-utils package provides several ways to integrate dbt testing into your data pipeline. The dbt-source command generates a visual dependency graph for each table. These tests are critical for ensuring data quality and can identify problems before they cause significant problems. The dbt-utils package also includes many additional tests. Here are some examples of these tests and how they are integrated into the pipeline:
Why Need to Integrate Dbt Testing Into Your Data Pipeline
Once you’ve built your project, you can run the dbt-test command on any table or model. Generic tests can be defined on any column and can be a property on any existing model. Similarly, you can create new test cases by modifying existing tests and adding new ones. It’s best to use the dbt-test command on a table or model when it’s ready for a change.
In addition, the dbt-testing utility supports writing custom tests against any data. A customized test script is defined in the test file. Afterwards, it can be run with the dbt test -data command. You can also define the test for any column or model that you wish. If you have an existing model and would like to run a custom test, you can add it as a property with the dbt-test-data command.
The dbt-testing module provides a simple example of a data test. It aggregates data from multiple orders and looks for records that don’t match the criteria. You can also create complex scenarios that involve logic and joins. The analysts are better suited to come up with scenarios to test. They’ll also know which use cases to look out for. Once you’ve configured the dbt-testing tool, you can begin executing test queries.
