dag-stack

Demo data pipeline with dbt, Airflow, Great Expectations.

See another possible architecture for this at https://github.com/astronomer/airflow-dbt-demo

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How to run

This repo contains a runnable demo using Astronomer (containerized Airflow), which is a convenient option to run everything in a Docker container.

• Install the Astronomer CLI (containerized Airflow), instructions here
  • Note: If you only want to run Airflow locally for development, you do not need to sign up for an Astronomer Cloud account. Simply follow the instructions to install the Astronomer CLI.
• Run astro dev start to start up the Airflow Docker containers
  • I had to follow the Docker config instructions here to handle a "buildkit not supported" error
  • I also had to reduce the number of AIRFLOW__WEBSERVER__WORKERS in the Dockerfile as well as allocate more resources to Docker in order for the webserver to run on my very old very slow laptop :) (2013 MacBook Air ftw)
  • Thanks to this post for the agate version pin to work with dbt
• This will start up the Airflow scheduler, webserver, and a Postgres database
• Once the webserver is up (takes about 30 seconds), you can access the Airflow web UI at localhost:8080
• You can run astro dev stop to stop the container again

You can also run the DAG in this repo with a standard Airflow installation if you want. You'll have to install the relevant dependencies (Airflow, dbt, Great Expectations, the respective operators, etc) and probably handle some more configurations to get it to work.

Development

In order to develop the dbt DAG and Great Expectations locally instead of in the containers (for faster dev loops), I created a new virtual environment with and installed relevant packages wit pip install -r requirements.txt

dbt setup

• Ran dbt init dbt to create the dbt directory in this repo
• I copied ~/.dbt/profiles.yml into the root of this project and added the Astronomer postgres creds to have a database available -- you wouldn't use this database in production or keep the file in the repo, this is just a shortcut for this demo!!
• The profiles.yml target setup allows me to run the dbt pipeline both locally and within the container:
  • Container:
    • connect to shell within the scheduler container
    • run cd dbt
    • run dbt run --profiles-dir /usr/local/airflow --target astro_dev
  • Local:
    • run cd dbt
    • run dbt run --profiles-dir /Users/sam/code/dag-stack --target local_dev

Great Expectations setup

• Ran great_expectations init to create the great_expectations directory in this repo
• Created Datasources for the data directory and the Astronomer postgres database using the great_expectations datasource new command
  • Note that I have two Datasources for the different host names, similar to the two dbt targets
  • I copied the credentials from uncommitted/config_variables.yml into the datasources section in great_expectations.yml for the purpose of this demo, since the uncommitted directory is git-ignored
• Created new Expectation Suites using great_expectations suite scaffold against the data_dir and postgres_local Datasources and manually tweaked the scaffold output a little using suite edit

Airflow DAG setup

• I'm using the custom dbt and Great Expectations Airflow operators, but this could also be done with Python and bash operators
• Note that the source data and loaded data validation both use the same Expectation Suite, which is a neat feature of Great Expectations -- a test suite can be run against any data asset to assert the same properties

Serving dbt and Great Expectations docs

• The DAG contains example tasks that copy the docs for each framework into the include folder in the container which is mapped to the host machine, so you can inspect them manually
• In production (and when deploying the container to Astronomer Cloud), both docs could (should) be copied to and hosted on an external service, e.g. on Netlify or in an S3 bucket

Additional resources

This repo is based on several existing resources:

• Great Expectations Airflow + dbt tutorial (which I had originally built)
• The example DAGs in the Great Expectations Airflow Provider (which I also originally built haha)
• Building a Scalable Analytics Architecture with Airflow and dbt