Why your dbt models need Data Contracts and how to add them

📘 Introduction

In this hands-on dbt tutorial, we'll walk you through why data contracts are critical for your dbt models — and how to add them to your dbt models step by step.

📌 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 is a Data Contract?

A data contract in dbt enforces that your model outputs match a defined schema, including column names and data types. This contract is defined in your model’s .yml schema file under the contract configuration. 

By enforcing this contract, dbt ensures your model won’t run if the output schema changes unexpectedly. This helps maintain data quality and prevents breaking downstream reports or processes. In essence, data contracts create a trusted boundary between data producers and consumers. This is essential for ensuring your downstream consumers (dashboards, pipelines, APIs) receive consistent and reliable data.

🧪1️⃣ Existing dbt model

We’ve already created a model called cleaned_course, and it has a corresponding .yml.

📝2️⃣ Add data contract to your schema.yml file

Now, let’s define a data contract in cleaned_course_mapping.yml.