Amazon EMR on EKS is now available in US West (N. California), US East (Ohio), Canada (Central), EU (Frankfurt and London), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, and Tokyo) regions

Starting today, Amazon EMR on Amazon EKS is now available in US West (N. California), US East (Ohio), Canada (Central), EU (Frankfurt and London), and Asia Pacific (Mumbai, Seoul, Singapore, Sydney, and Tokyo) regions.

Amazon EMR on EKS allows customers to automate the provisioning and management of open-source big data frameworks on EKS. With EMR on EKS, customers can now run Spark applications alongside other types of applications on the same EKS cluster to improve resource utilization and simplify infrastructure management. Customers can deploy EMR applications on the same EKS cluster as other types of applications, which allows them to share resources and standardize on a single solution for operating and managing all their applications. Customers get access to the same EMR capabilities on EKS that they use on Amazon EC2 today, such as access to the latest performance optimized Spark runtime, EMR Studio (preview) for application development, and a persistent Spark UI for debugging.

To get started, register your EKS cluster with Amazon EMR. Then define your job including EMR release version, Spark parameters, and application dependencies. Amazon EMR on Amazon EKS will schedule the pods, containers, and resources onto your Amazon EKS cluster. You can configure your job to run on Amazon EC2 instances, or Amazon Fargate if you want a serverless experience. You can create workflows with Amazon Managed Workflows for Apache Airflow and analyze output with per job logs stored in Amazon S3 or Amazon CloudWatch.

You can learn more by reading our Amazon on EKS Launch Blog, our Amazon EMR on EKS documentation or visit the Amazon EMR on Amazon EKS detail page.