Spot helps you reduce your AWS bill with minimal changes to your infrastructure. Spot instances are spare EC2 capacity offered to you at a discounted price compared to On-Demand Instance prices. If you run these workers on Spot instances, you can reduce the cost of running your Airflow cluster by up to 90%. Whether your workflow is an ETL job, a media processing pipeline, or a machine learning workload, an Airflow worker runs it. In contrast, the UI and scheduler are lightweight processes (by default, they are allocated 1.5 vCPUs and 1.5 GB memory combined). Workers do the heavy lifting in an Airflow cluster. In a typical Airflow cluster, Airflow workers’ resource consumption outweighs that of Airflow core components by far. Second, the infrastructure that’s required to run Airflow workers, which execute workflows. First, the infrastructure needed to run Airflow’s core components such as its web-UI and scheduler. The infrastructure required to run Airflow can be put into two categories. This post shows you how you can operate a self-managed Airflow cluster using Amazon Elastic Kubernetes Service (EKS) and optimize it for cost using EC2 Spot Instances. Many AWS customers choose to run Airflow on containerized environments with tools such as Amazon EKS or Amazon ECS because they make it easier to manage and autoscale Airflow clusters. It is designed to be extensible, and it’s compatible with several services like Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and Amazon EC2. Apache Airflow is an open-source distributed workflow management platform for authoring, scheduling, and monitoring multi-stage workflows.
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