Apache Airflow is a popular open-source platform for orchestrating workflows or pipelines. You can programmatically define data or task pipelines, schedule when they run, and monitor how they perform – all from one platform. Apache Airflow makes it easy to manage complex data pipelines.

How Apache Airflow Helps Users

With Apache Airflow, you can:

• Define and orchestrate complex workflows and data pipelines as DAGs (Directed Acyclic Graphs) using Python
• Schedule tasks to run at specific intervals or in response to triggers
• Visualize your data pipelines' dependencies, progress, logs, code, trigger tasks, and success status, all through a web-based UI

Airflow supports integration with cloud platforms, databases, containerized environments, and external APIs through a rich ecosystem of providers and plugins.

Why Integrate Apache Airflow with emma

By integrating Airflow with emma, teams can:

• Gain visibility into Airflow DAG performance and resource consumption across multi-cloud environments
• Monitor the infrastructure impact of pipeline executions and correlate with cost data
• Receive automated recommendations from emma to rightsize compute environments and reduce overprovisioning
• Trigger workflow execution or scale actions based on real-time insights, anomalies, or thresholds detected by emma

Integrate Apache Airflow with emma to improve workflow efficiency, control infrastructure costs, and intelligently distribute workflows across multi-cloud environments.