Using Dagster with dbt: A Modern Approach to Data Orchestration

📘 Introduction

In this hands-on tutorial, we’ll walk you step-by-step through how to orchestrate your dbt workflows using Dagster. Combining Dagster’s robust orchestration capabilities with dbt’s transformation power allows you to build reliable, maintainable, and observable data pipelines. 

✅ Prerequisites

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

💡 What is Dagster and why combine it with dbt?

Dagster is an open-source data orchestrator that helps you design, schedule, and monitor data pipelines. While dbt focuses on transforming your data using SQL-based models, Dagster allows you to orchestrate these transformations, manage dependencies, and integrate them into larger workflows. Combining Dagster and dbt gives you the best of both worlds: dbt’s transformation power and Dagster’s pipeline orchestration and observability.

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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📥1️⃣ Install Dagster for dbt

To get started, activate your virtual environment and install the Dagster-dbt integration via pip. Open a terminal and run the following command: