Basic dbt Tutorial

A basic example DAG from the Astronomer tutorial featuring the execution of dbt commands in Airflow.

Data Processing


Providers:

Modules:

Run this DAG

Download the repository:

Install Astronomer CLI:Skip if you already have our CLI

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.

Notes

  • 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 Astronomer's

containerized postgres database solely for the purpose of this demo. In a production environment, you should use a production-ready database and use environment variables or some other form of secret management for the database credentials.

  • 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.