![]() # Set dependency to run tests on a model after model runs finishesĭbt_tasks > dbt_tasks įor upstream_node in data : replace ( "model", "test" )ĭbt_tasks = make_dbt_task ( node, "run" )ĭbt_tasks = make_dbt_task ( node, "test" ) dbt import DbtCloudRunJobOperatorįrom airflow. dbt import DbtCloudHook, DbtCloudJobRunStatusįrom airflow. python import ShortCircuitOperatorįrom airflow. If the job is currently not in a state of 10 (Success), 20 (Error), or 30 (Canceled), the pipeline will not try to trigger another run.įrom airflow. The operational check uses this method to retrieve the latest triggered run for a job and check its status. The DbtCloudHook provides a list_job_runs() method which can be used to retrieve all runs for a given job. This example showcases how to run a dbt Cloud job from Airflow, while adding an operational check to ensure the dbt Cloud job is not running prior to triggering. In the DAG below, you'll review a simple implementation of the dbt Cloud provider. If you want your dbt Cloud Provider tasks to use a default account ID, you can add that to the connection, but it is not required. The connection type should be dbt Cloud, and it should include an API token from your dbt Cloud account. Set up an Airflow connection to your dbt Cloud instance.If you are working in an Astro project, you can add the package to your requirements.txt file. Add the apache-airflow-providers-dbt-cloud package to your Airflow environment.To use the dbt Cloud provider in your DAGs, you'll need to complete the following steps: ![]() DbtCloudHook: Interacts with dbt Cloud using the V2 API.DbtCloudJobRunSensor: Waits for a dbt Cloud job run to complete.DbtCloudGetJobRunArtifactOperator: Downloads artifacts from a dbt Cloud job run.DbtCloudRunJobOperator: Executes a dbt Cloud job.To orchestrate dbt Cloud jobs with Airflow, you can use the dbt Cloud provider, which contains the following useful modules: See Managing your Connections in Apache Airflow. Airflow fundamentals, such as writing DAGs and defining tasks.To get the most out of this tutorial, make sure you have an understanding of: For more information, check out Part 1, Part 2, and Part 3 of the series. The dbt Core sections of this guide summarize some of the key practices and findings from our blog series with Updater and Sam Bail about using dbt in Airflow. Learn how to extend the model-level use case by automating changes to a dbt model.Review two common use cases for orchestrating dbt Core with Airflow with the BashOperator:.Use the dbt Cloud Provider to orchestrate dbt Cloud with Airflow.Airflow also gives you fine-grained control over dbt tasks such that teams have observability over every step in their dbt models. Running dbt with Airflow ensures a reliable, scalable environment for models, as well as the ability to trigger models only after every prerequisite task is met. Organizations can use Airflow to orchestrate and execute dbt models as DAGs. Now, data engineers can use dbt to write, organize, and run in-warehouse transformations of raw data. As ephemeral compute becomes more readily available in data warehouses thanks to tools like Snowflake, dbt has become a key component of the modern data engineering workflow. Dbt is an open-source library for analytics engineering that helps users build interdependent SQL models for in-warehouse data transformation.
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