When should I use dbt unit tests instead of data tests?

Use unit tests to validate the internal logic of your SQL (like complex joins or math) on small, static inputs during development. Use data tests to validate the quality and integrity of actual production data in your warehouse.

Does this skill support Python models in dbt?

No, dbt currently only supports unit testing for SQL models, and this skill follows that standard implementation.

What data formats are supported for mocking inputs?

The skill supports YAML dictionaries (default), CSV files or inline text, and SQL queries/fixtures for complex data types or ephemeral model dependencies.

Can I use this for incremental models?

Yes, dbt unit tests support validating the logic of what will be merged or inserted for incremental models, though they do not test the final state of the database table after the merge.

dbt Unit Testing

Name: dbt Unit Testing
Author: dbt-labs

bydbt-labs

•

Security & Testing

Streamlines the creation and configuration of unit tests for dbt models to ensure SQL logic reliability and facilitate TDD.

About

This skill provides specialized guidance for implementing unit tests within dbt projects, enabling developers to validate SQL modeling logic on static inputs before materializing models in production. It facilitates Test-Driven Development (TDD) by automating the generation of Model-Input-Output scenarios, mocking data dependencies using various formats like YAML, CSV, or SQL, and handling complex logic cases such as window functions, regex, and date math. By ensuring unit tests pass before deployment, this skill improves code reliability and optimizes warehouse compute costs during CI/CD processes.

Key Features

Automated unit test configuration for dbt models
Mocking of model dependencies using ref() and source()
Support for YAML, CSV, and SQL mock data formats
Validation of complex SQL logic including window functions and regex
13 GitHub stars
Guidance for TDD workflows and regression testing

Use Cases

Ensuring logic consistency during significant refactoring of critical data models
Validating complex SQL transformations and edge cases before production materialization
Implementing Test-Driven Development (TDD) for new dbt models

About

Key Features

Automated unit test configuration for dbt models
Mocking of model dependencies using ref() and source()
Support for YAML, CSV, and SQL mock data formats
Validation of complex SQL logic including window functions and regex
13 GitHub stars
Guidance for TDD workflows and regression testing

Use Cases

Ensuring logic consistency during significant refactoring of critical data models
Validating complex SQL transformations and edge cases before production materialization
Implementing Test-Driven Development (TDD) for new dbt models