Describing and Documenting your Data in schema.yml in dbt

📘Introduction

In this hands-on dbt tutorial, we'll walk you through how to describe and document your data models using schema.yml in dbt.

📌 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 schema.yml?

The schema.yml file in dbt is used to document your models, columns, and sources in a structured way. In addition to descriptions, it supports tests, data contracts, grants, and custom metadata - enabling teams to enforce data quality, access control, and governance policies. This makes your dbt project more reliable, secure, and collaborative.

🧪1️⃣ Create a dbt model

We've already created a dbt model called cleaned_student.

The model contains the following SQL code:

SELECT
    ID AS id,
    Name AS name,
    Major AS major,
    Number AS number,
    'udc' AS source_name,
FROM {{ source("udc", "student") }}

📝2️⃣ Create schema.yml

Now let’s document this model. To do this, create a file named schema.yml in your models/ folder.