Category: Amazon EMR

Amazon EMR Serverless now supports a 32 vCPU / 244 GB worker configuration for the most demanding Spark jobs. Across 126 TPC-DS and TPC-H queries, larger workers delivered an average 29% faster query execution and 29% lower cost, with the biggest gains on shuffle-heavy, multi-table join queries.

Learn how to automate Apache Spark 3.x to 4.0 Scala migration on Amazon EMR using the AWS Spark Upgrade Agent. This post covers API deprecations, behavioral changes, build configuration updates, and job validation, turning months of manual effort into hours.

In this post, we show how Apache Celeborn resolves this trade-off for Amazon EMR on EKS and Amazon EMR on EC2, improving job reliability while unlocking additional cost savings.

This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.

In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

In this post, we walk through a hands-on PySpark migration from Spark 3.5 to Spark 4.0 on Amazon EMR Serverless, using the AWS Spark Upgrade Agent. You’ll see how the agent iteratively validates your application on a live Amazon EMR Serverless application, automatically diagnosing and resolving failures from Amazon CloudWatch logs until the job succeeds.

Today, AWS is announcing support for Spark Connect on Amazon EMR Serverless with EMR release 7.13 (Apache Spark 3.5.6) and later versions. You can now build and debug Spark applications from your preferred local environment while running full-scale Spark operations on EMR Serverless.

In this post, we demonstrate how to build a production-ready IoT device monitoring system using Spark 4.0’s transformWithState API on Amazon EMR Serverless. This example showcases the key capabilities of stateful streaming and provides a template you can adapt for your own use cases.

With this general availability announcement, Spark 4.0 is now supported across Amazon EMR Serverless, Amazon EMR on EC2, and Amazon EMR on EKS deployment options. In this post, you’ll learn about key Spark 4.0 capabilities now available on Amazon EMR including Spark Connect, the Variant data type, SQL scripting, Python API improvements, and streaming enhancements, along with infrastructure changes in the new emr-spark-8.0 release.