First developed by Airbnb, it is now under the Apache Software Foundation. Profiles_path = Path( '/opt/airflow/example_dbt_project/profiles'),Įxecution_operator = ExecutionOperator.Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. Manifest_path = Path( '/opt/airflow/example_dbt_project/target/manifest.json'), Project_path = Path( '/opt/airflow/example_dbt_project/'), 'model.example_dbt_project.int_revenue_by_date', 'python_callable': lambda: print( 'Hello world 3!'), 'snapshot.example_dbt_project.int_customers_per_store_snapshot', 'test.example_dbt_project.int_customers_per_store', 'python_callable': lambda: print( 'Hello world 2!'), 'model.example_dbt_project.int_revenue_by_date' 'model.example_dbt_project.int_customers_per_store', 'python_callable': lambda: print( 'Hello world'), execution import ExecutionOperator with DAG( task_group import DbtTaskGroup from dbt_airflow. config import DbtAirflowConfig, DbtProjectConfig, DbtProfileConfig from dbt_airflow. dummy import DummyOperator from dbt_airflow. python import PythonOperator from airflow. The package is available on PyPI and can be installed through pip:įrom datetime import datetime from pathlib import Path from airflow import DAG from airflow. Package expects that you have already compiled your dbt project so that an up to date manifest file can then be used This means that you first need to compile (or runĪny other dbt command that creates the manifest file) before creating your Airflow DAG. The target/manifest.json file in your dbt project directory. The library essentially builds on top of the metadata generated by dbt-core and are stored in Create sub- TaskGroups of dbt Airflow tasks based on your project's folder structure.Introduce extra tasks within the dbt project tasks and specify any downstream or upstream dependencies.Add tasks before or after the whole dbt project.Every model, seed and snapshot resource that has at least a single test, will also have a corresponding.Render a dbt project as a TaskGroup consisting of Airflow Tasks that correspond to dbt models, seeds, snapshots.Here's how the popular Jaffle Shop dbt project will be rendered on Apache Airflow via dbt-airflow: Everyĭbt model, seed, snapshot or test will have its own Airflow Task so that you can perform any action at a task-level. Render their dbt projects in a granular level such that they have full control to individual dbt resource types. Additionally, it would beat the purpose of dbt, that among other features, it also automates modelĭbt-airflow is a package that builds a layer in-between Apache Airflow and dbt, and enables teams to automatically If we were about to do this work manually, we would have to put huge effort that would also be Since re-running the whole project will be time-consuming and expensive.Ī potential solution to this problem is to create individual Airflow tasks for every model, seed, snapshot and test That will build and test data models will consist of two tasks, one that executes dbt run command followed by anīut what happens when model builds or tests fail? Should we re-run the whole dbt project (that could involve hundreds ofĭifferent models and/or tests) just to run a single model we've just fixed? This doesn't seem to be a good practice One way to host dbt projects and orchestrate dbt tasks is via Apache Airflow. Run dbt commands from their local machine (or even a host machine), but how do you know if a model run by anotherĬontributor has failed, or succeeded? How can you enable shared visibility over data models, within the team? Tool, but if used as is, it will create silos in the way an organisation manages its data. Theīiggest challenge though is how to embed dbt in modern data workflows and infrastructure. Models, seeds, snapshots and tests are represented by individual Airflow Task.ĭbt is a command-line tool that enables data teams build, maintain and test data models in a scalable fashion. A Python package that helps Data and Analytics engineers render dbt projects in Apache Airflow DAGs such that
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