Category: Amazon EMR

Learn how Moovit modernized its data platform with a multi-engine lakehouse architecture: offloading heavy aggregation workloads from Amazon Redshift to Amazon EMR with Spark SQL, isolating workloads with Amazon Redshift Serverless, and cutting overall data pipeline cost by 33%.

Learn how to query Amazon S3 Tables from Trino on Amazon EMR using the Apache Iceberg REST catalog endpoint. This post shows how to deploy the integration with AWS CloudFormation, configure the Trino catalog, and run SQL to create, query, and manage Apache Iceberg tables.

Extend AWS DevOps Agent to investigate Apache Spark failures on Amazon EMR. This post shows how to register the Apache Spark Troubleshooting Agent for Amazon EMR as a custom MCP capability provider over AWS PrivateLink, so a single agent chat session diagnoses a failing Spark job from an Amazon CloudWatch alarm to a line-numbered root cause.

Amazon EMR on EKS now runs Apache Spark up to 3.7x faster on Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell GPUs than on comparable CPU instances, with no changes to existing Spark code. See the TPC-DS benchmark results, the cost comparison, and how to get started.

In this post, we show you how to set up the agent for Amazon EMR on EKS and walk through troubleshooting a failed job run. We demonstrate the workflow from both the Amazon EMR console and an AI assistant that supports the Model Context Protocol (MCP), an open standard for connecting AI assistants to external tools and data.

In this post, we discuss the benefits of Advanced Scaling for Amazon EMR on Amazon EC2 and demonstrate how it works through some example scenarios. You’ll learn when to prioritize utilization optimized settings for cost savings with conservative scaling, balanced approaches for mixed workloads, or performance optimized configurations for SLA-sensitive jobs requiring aggressive scaling.

Processing encrypted data in Amazon S3 with Amazon EMR and Apache Spark can drive up AWS KMS decrypt API costs as the number of objects grows. This post shows three techniques to reduce those costs without compromising encryption: optimizing file formats (including Apache Iceberg), aggregating data, and using AWS Glue Data Catalog partition indexes.

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.