How to Test Your Data Models with dbt

#5: Data Pipeline Summer

Data models are only as good as the data quality checks that ensure confidence in them. Without data quality checks on your model, you will never know if your data is being transformed as expected.

Testing acts as checks and balances put in place by analytics engineers. Ideally, tests never fail and you have nothing to worry about. However, if they do, you’re immediately alerted, preventing issues from going unnoticed.

In the last edition of Data Pipeline Summer, you learned how to write an incremental model with dbt. In today’s newsletter, you will learn how to properly test sources and models using dbt generic tests and available open-source packages.