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