Scheduling dbt models with Dagster: A Step-by-Step Guide

📘 Introduction

In this hands-on tutorial, we’ll walk step-by-step through scheduling dbt models with Dagster. By integrating Dagster, you can easily orchestrate your dbt workflows and automating model runs.

✅ Prerequisites

🐍☑️ Installed Python
📦☑️Installed Pip
🌐☑️ Created a virtual environment (venv)
🗂️☑️ A dbt project set up
🧪☑️ Existing dbt models defined in your models/ directory
🐙☑️ Created a Dagster project within your dbt project

🗂️ Models in our dbt project

In this tutorial, we’re working with a dbt project called dlnerds_university. The project contains several dbt models and a Dagster project named dlnerds_university_dagster.

Now, we want to schedule the models with Dagster.

🚪1️⃣ Open schedules.py in your Dagster project

Navigate to your Dagster project folder and open schedules.py. This is where we’ll define when dbt models should run.

📥2️⃣ Import modules in schedules.py

At the top of schedules.py, add:

from dagster_dbt import build_schedule_from_dbt_selection

from .assets import dlnerds_university_dbt_assets

This lets Dagster know where to find your dbt assets.

If you’d like to dive deeper into dbt (data build tool), our book Building Modern Data Pipelines with dbt: From Raw Data to Gold Standard with the Medallion Architecture provides a hands-on guide to designing modern data pipelines. It covers dbt’s core concepts and best practices, including building Bronze, Silver, and Gold layers with the Medallion Architecture. It also serves as a hands-on study guide for the dbt Analytics Engineering Certification.

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🛠️3️⃣ Add cron-like schedule in schedules.py

Next, let’s tell Dagster to run our dbt models every morning at 6 AM: