How to add generic data tests to your dbt models: Improve Data Quality with Confidence

📘 Introduction

In this hands-on dbt tutorial, we'll walk you through how to add and run generic data tests in dbt using YAML configuration. These built-in tests help ensure your data quality and integrity, without writing any custom SQL.

📌 This is a must-know topic for the dbt Analytics Engineering Certification Exam, so mastering it now puts you one step closer to passing the exam and leveling up your data engineering skills! 👨‍🎓

✅ Prerequisites

Before you start, make sure you have:

☑️ A dbt project set up
☑️ At least one model defined in your models/ directory

💡What are generic data tests?

Generic tests are reusable, built-in dbt tests that help catch common data issues. Some of the most commonly used ones include:

• not_null: Ensures a column has no null values
• unique: Ensures values in a column are unique
• accepted_values: Restricts a column to only specific allowed values
• relationships: Validates referential integrity between models (like foreign keys)

🧪1️⃣ Existing dbt model

We’ve already created a model called cleaned_course_mapping, and it has a corresponding .yml file for documentation and testing.

After running the model, the table in the warehouse looks like this:

As you can see, there are duplicates in the code column - this will be important later when we test for uniqueness.

📝2️⃣ Add data tests to your schema.yml file

Now, let’s define generic tests in cleaned_course_mapping.yml.