Use Cases | Apache Airflow

Apache Airflow® can orchestrate most anything!

Apache Airflow® allows you to define almost any workflow in Python code, no matter how complex. Because of its versatility, Airflow is used by companies all over the world for a variety of use cases. This section presents the four most common use cases for Airflow, but the possibilities are endless.

Many companies build their core business, data powered applications, on top of Apache Airflow.

Airflow is the open source standard for orchestrating ETL/ELT data pipelines.

With Airflow you can spin up, manage and tear down your infrastructure at the exact time you need it.

Airflow is the heart of the modern MLOps stack, orchestrating the entire machine learning lifecycle.

Apache Airflow® helps businesses around the world reach their goals.

Deploying Airflow allowed us to efficiently manage workloads with multiple DAGs, from generating reports and system …

Apache Airflow is highly extensible and its plugin interface can be used to meet a variety of use cases. It supports …

Apache Airflow helped us scale from 10 to 100+ users across 20+ teams with a variety of use cases. By writing our own …

Apache Airflow is a great open-source workflow orchestration tool supported by an active community. It provides all the …

Airflow is Batteries-Included. A great ecosystem and community that comes together to address about any (batch) data …

Airflow can be an enterprise scheduling tool if used properly. Its ability to run "any command, on any node" is amazing. …

Airflow is extensible enough for any business to define the custom operators they need. Airflow can help you in your …

Apache Airflow helps us efficiently tackle crucial game dev tasks, such as working with churn or sorting bank offers.

Apache Airflow helps us orchestrate the construction and release of our curated collection of containers

Airflow helped us increase the visibility of our batch processes, decouple our batch jobs, and improve our development …

Airflow helped us to define and organize our ML pipeline dependencies, and empowered us to introduce new, diverse batch …

Airflow implementation resulted in significant time savings, increased productivity, and improved operational efficiency …