A better way to use airflow by dynamically generating DAGs using factories.
If you’ve been on the hunt for a workflow management platform, my guess is you’ve come across Apache Airflow already. Originally hailing from the corner of Airbnb, this widely used project is now under the Apache banner and is the tool of choice for many data teams. Airflow allows you to write complex workflows in a declarative manner and offers many out-of-box operators to do complex tasks.
At Flywheel Software we heavily use Airflow for complex ETL pipelines, machine learning pipelines, and statistical analysis. Moreover, we manage multiple airflow deployments and also run massive multi-tenant airflow clusters running a plethora of workloads. As we began to push airflow to its limits we recently undertook a reworking of how we deploy to airflow clusters — and dare I say found a better way to use airflow.
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