Tens of thousands of customers use Amazon EMR to run big data analytics applications on frameworks such as Apache Spark, Hive, HBase, Flink, Hudi, and Presto at scale. EMR automates the provisioning and scaling of these frameworks and optimizes performance with a wide range of EC2 instance types to meet price and performance requirements. Customer are now consolidating compute pools across organizations using Kubernetes. Some customers who manage Apache Spark on Amazon Elastic Kubernetes Service (EKS) themselves want to use EMR to eliminate the heavy lifting of installing and managing their frameworks and integrations with AWS services. In addition, they want to take advantage of the faster runtimes and development and debugging tools that EMR provides.

Today, we are announcing the general availability of Amazon EMR on Amazon EKS, a new deployment option in EMR that 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.

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