Amazon EMR Serverless is a deployment option for Amazon EMR that you can use to run open source big data analytics frameworks such as Apache Spark and Apache Hive without having to configure, manage, or scale clusters and servers. EMR Serverless integrates with Amazon Web Services (AWS) services across data storage, streaming, orchestration, monitoring, and governance to provide a comprehensive serverless analytics solution.
In this post, we share the top 10 best practices for optimizing your EMR Serverless workloads for performance, cost, and scalability. Whether you’re getting started with EMR Serverless or looking to fine-tune existing production workloads, these recommendations will help you build efficient, cost-effective data processing pipelines. The following diagram illustrates an end-to-end EMR Serverless architecture, showing how it integrates into your analytics pipelines.
1. Define applications one time, reuse multiple times
https://aws.amazon.com/blogs/big-data/top-10-best-practices-for-amazon-emr-serverless/
