![]() ![]() ADF jobs can be run using an Airflow DAG, giving the full capabilities of Airflow orchestration beyond using ADF alone. Custom packages and dependencies can be complex to manage.Integrations with services outside of Azure are limited.Building and integrating custom tools can be difficult.However, it has some disadvantages when used alone - namely: It integrates seamlessly with on-premises data sources and other Azure services. ![]() Why use Airflow with ADF ĪDF is an easy to learn tool that allows you to quickly create jobs without writing code. To learn more about all of the ADF modules in the Microsoft Azure provider, check out the Astronomer Registry. The DAG will execute both ADF pipelines in parallel (tasks run_pipeline1 and run_pipeline2), and then will use an AzureDataFactoryPipelineRunStatusSensor to wait until pipeline2 has completed before finishing the DAG. ![]() Go to the Airflow UI, unpause your example_adf_run_pipeline DAG, and trigger it to run the your ADF pipelines. Step 5: Run your DAG to execute your ADF pipelines The DAG graph should look similar to this:
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