dbt with unit testing

Its main strength lies in managing table dependencies (through the declaration of references and sources), refactoring via macros, and integrating documentation. dbt also allows the definition of unit tests that help validate the proper execution of standard SQL queries. With this feature, it is possible to simulate data using CSV files, known as seeds, and compare transformation results against expected outcomes. This helps establish and maintain best development practices directly within SQL queries over time. This is a key differentiator compared to other solutions like Google DataFlow or AWS Data Pipeline.

However, creating these unit tests comes with some challenges to keep in mind:

• Multiplication of mock files (CSV) to ensure full coverage.
• The need to maintain consistency between mocked tables (IDs, relationships between tables).
• Complex test management for advanced operations (joins, group by).

Theodo’s point of view

We recommend dbt for building robust and maintainable pipelines, thanks to its unit testing capabilities that support continuous development and prevent legacy code buildup. However, for massive data processing or highly specific use cases, tools like Apache Spark or DataFlow may be more suitable, even though dbt stands out for its best practices.