Airflow is built to scale, with data teams at OpenAI and GitHub running thousands to millions of tasks every day. But as deployments multiply, so does the operational burden: managing disjointed deployments across different systems, manually provisioning workers, and debugging failures across dependencies.

In this webinar, we'll share the operational best practices that keep Airflow running reliably as your deployments grow.

You'll learn how to:

• Automate managing multiple Airflow deployments and users with programmatic control
• Use scaling parameters to right-size workers and optimize resource utilization across environments
• Monitor multiple Airflow deployments with full pipeline observability and lineage, so debugging takes minutes, not hours