Customize and Package Dependencies With Your Apache Spark Applications on Amazon EMR on Amazon EKS

Last AWS re:Invent, we announced the general availability of Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS), a new deployment option for Amazon EMR that allows customers to automate the provisioning and management of Apache Spark on Amazon EKS.

With Amazon EMR on EKS, customers can deploy EMR applications on the same Amazon 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 running Apache Spark on Kubernetes can migrate to EMR on EKS and take advantage of the performance-optimized runtime, integration with Amazon EMR Studio for interactive jobs, integration with Apache Airflow and AWS Step Functions for running pipelines, and Spark UI for debugging.

When customers submit jobs, EMR automatically packages the application into a container with the big data framework and provides prebuilt connectors for integrating with other AWS services. EMR then deploys the application on the EKS cluster and manages running the jobs, logging, and monitoring. If you currently run Apache Spark workloads and use Amazon EKS for other Kubernetes-based applications, you can use EMR on EKS to consolidate these on the same Amazon EKS cluster to improve resource utilization and simplify infrastructure management.

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