Library to convert DBT manifest metadata to Airflow tasks

Read the full documentation at https://dbt-airflow-factory.readthedocs.io/

Use the package manager pip to install the library:

pip install dbt-airflow-factory

The library is expected to be used inside an Airflow environment with a Kubernetes image referencing dbt.

dbt-airflow-factory's main task is to parse manifest.json and create Airflow DAG out of it. It also reads config files from config directory and therefore is highly customizable (e.g., user can set path to manifest.json).

To start, create a directory with a following structure, where manifest.json is a file generated by dbt:

.
├── config
│   ├── base
│   │   ├── airflow.yml
│   │   ├── dbt.yml
│   │   └── k8s.yml
│   └── dev
│       └── dbt.yml
├── dag.py
└── manifest.json

Then, put the following code into dag.py:

from dbt_airflow_factory.airflow_dag_factory import AirflowDagFactory
from os import path

dag = AirflowDagFactory(path.dirname(path.abspath(__file__)), "dev").create()

When uploaded to Airflow DAGs directory, it will get picked up by Airflow, parse manifest.json and prepare a DAG to run.

It is best to look up the example configuration files in tests directory to get a glimpse of correct configs.

You can use Airflow template variables in your dbt.yml and k8s.yml files, as long as they are inside quotation marks:

target: "{{ var.value.env }}"
some_other_field: "{{ ds_nodash }}"

Analogously, you can use "{{ var.value.VARIABLE_NAME }}" in airflow.yml, but only the Airflow variable getter. Any other Airflow template variables will not work in airflow.yml.

DBT Airflow Factory works best in tandem with data-pipelines-cli tool. dp not only prepares directory for the library to digest, but also automates Docker image building and pushes generated directory to the cloud storage of your choice.