Observing Airflow DAGs in Prefect
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You'll learn how to monitor Airflow DAGs in Prefect with specific code you can copy-paste into your setup. By the end of this webinar, you'll have the tools to use Prefect's alerting and error handling system to process the failures occurring within your Airflow data workflows.
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Hear what our clients have to say and explore additional resources that dive into the differences between Prefect and Airflow.
Testimonial
“Airflow was no longer a viable option for Machine Learning workflows ... we needed an orchestration platform that offers a high level of data security and can be easily adopted by ML practitioners. ”
Wendy Tang
Machine Learning Engineer, Cash App
Testimonial
“Prefect's compute model associates resources with jobs rather than environments, enabling us to run diverse workflows in a unified environment. The ability to separate code and infrastructure is extremely impressive - you can define everything in a single YAML file, making it easy to specify exactly which resources you need.”
Sunny Pachunuri
Data Engineering / Platform Manager, Endpoint
