In the following sections, we describe an approach that enables you to do that.Īlthough the example in this post discusses how you can get a cost estimate for applications running on EMR clusters, you can also use the approach if you’re running a Spark or Hive application elsewhere, and want to estimate the cost of running it on EMR Serverless. However, the Amazon EMR pricing page doesn’t tell you how you can easily estimate the cost of running your existing EMR cluster applications on EMR Serverless. This approach helps you evaluate and adopt the deployment option that is most cost effective for the application. In this post, we discuss how you can estimate what it may cost to run an application that currently runs on EMR clusters using the new serverless option, and perform this analysis simply by using your current application metrics. Many customers already run data analytics applications on EMR clusters, and find that the new serverless option is simpler and less expensive. You can also learn about the pricing for these options from the Amazon EMR pricing page. To learn about the benefits of each deployment option in EMR Serverless, refer to What are some of the feature differences between EMR Serverless and Amazon EMR on EC2? in the Amazon EMR FAQ. When you build a Spark or Hive application using an Amazon EMR release, say Amazon EMR 6.8, you can run the application on EMR clusters, on EKS clusters using Amazon EMR on EKS, or using EMR Serverless without having to change the application. With Amazon EMR, you can run your analytics applications on dedicated EMR clusters, on existing Amazon Elastic Kubernetes Service (Amazon EKS) clusters, or using the new EMR Serverless deployment option where you don’t have to manage clusters or instances. You get all the features of the latest open-source frameworks with the performance-optimized runtime of Amazon EMR, and without having to plan and operate instances and clusters. Amazon EMR Serverless is a serverless option in Amazon EMR that makes it easy for data analysts and engineers to run applications using open-source big data analytics frameworks such as Apache Spark and Hive without configuring, managing, and scaling clusters or servers.
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