Data teams at companies like Uber, Ford, and LinkedIn use Airflow to power their data ops because of its ability to scale, sometimes orchestrating hundreds of thousands of tasks each day.
However, running multiple Airflow deployments at scale can be a challenge. Without properly optimizing resource utilization, task performance, and user management, teams can encounter bottlenecks and suboptimal developer efficiency.
Join our live session to learn best practices for running Airflow at scale. We’ll cover:
- How to choose appropriate Airflow resources and configurations for your workloads.
- How to automate management of Airflow deployment resources and users.
How to leverage observability and alerting features to monitor the health of your Airflow deployments.
