Amazon EMR Serverless adds job-level billed resources for efficient cost management
Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks like Apache Spark and Apache Hive without configuring, managing, and scaling clusters or servers. Starting today, you can view the aggregated Billed resource utilization for each job within an EMR Serverless application, simplifying the cost calculation per job run.
Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks like Apache Spark and Apache Hive without configuring, managing, and scaling clusters or servers. Starting today, you can view the aggregated Billed resource utilization for each job within an EMR Serverless application, simplifying the cost calculation per job run.
When running Spark or Hive workloads, it is useful to see the resources used by individual job runs to help understand and manage your total costs. With this feature, you can get a detailed view of the vCPU-hours, memoryGB-hours, and storageGB-hours consumed by an EMR serverless job on completion. Using this data and the pricing in each Region, you can accurately calculate the cost per job run. You can view these values both in the EMR Studio UI and the GetJobRun API.
The Billed resource utilization feature is available in all regions where Amazon EMR Serverless is available.