Advanced dbt Tutorial

An advanced example DAG from the Astronomer tutorial featuring the execution of dbt commands in Airflow.

Data Processing



Last Updated: Aug. 26, 2021

Run this DAG

1. Install the Astronomer CLI:Skip if you already have our CLI

2. Download the repository:

3. Navigate to where the repository was cloned and start the DAG:

Airflow DAGs for dbt

The code in this repository is meant to accompany this blog post on beginner and advanced implementation concepts at the intersection of dbt and Airflow.

To run these DAGs locally:

  1. Download the Astro CLI
  2. Download and run Docker
  3. Clone this repository and cd into it.
  4. Run astro dev start to spin up a local Airflow environment and run the accompanying DAGs on your machine.

dbt project setup

We are currently using the jaffle_shop sample dbt project. The only files required for the Airflow DAGs to run are dbt_project.yml, profiles.yml and target/manifest.json, but we included the models for completeness. If you would like to try these DAGs with your own dbt workflow, feel free to drop in your own project files.


  • To use these DAGs, Airflow 2.2+ is required. These DAGs have been tested with Airflow 2.2.0.
  • If you make changes to the dbt project, you will need to run dbt compile in order to update the manifest.json file.

This may be done manually during development, as part of a CI/CD pipeline, or as a separate step in a production pipeline run before the Airflow DAG is triggered.

  • The sample dbt project contains the profiles.yml, which is configured to use environment variables. The

database credentials from an Airflow connection are passed as environment variables to the BashOperator tasks running the dbt commands.

  • Each DAG runs a dbt_seed task at the beginning that loads sample data into the database. This is simply for the purpose of this demo.