Category: Amazon EMR

In this post, we demonstrate how to implement Apache Spark Connect on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) to build decoupled data processing applications. We show how to set up and configure Spark Connect securely, so you can develop and test Spark applications locally while executing them on remote Amazon EMR clusters.

In this post, we show you how the Apache Spark troubleshooting agent helps analyze Apache Spark issues by providing detailed root causes and actionable recommendations. You’ll learn how to streamline your troubleshooting workflow by integrating this agent with your existing monitoring solutions across Amazon EMR and AWS Glue.

In this post, you learn how to assess your existing Amazon EMR Spark applications, use the Spark upgrade agent directly from the Kiro IDE, upgrade a sample e-commerce order analytics Spark application project (including build configs, source code, tests, and data quality validation), and review code changes before rolling them out through your CI/CD pipeline.

In this post, we demonstrate how Apache Hive on Amazon EMR 7.10 delivers significant performance improvements for both read and write operations on Amazon S3.

Starting with version 7.10, Amazon EMR is transitioning from EMR File System (EMRFS) to EMR S3A as the default file system connector for Amazon S3 access. This transition brings HBase on Amazon S3 to a new level, offering performance parity with EMRFS while delivering substantial improvements, including better standardization, improved portability, stronger community support, improved performance through non-blocking I/O, asynchronous clients, and better credential management with AWS SDK V2 integration. In this post, we discuss this transition and its benefits.

Socure is one of the leading providers of digital identity verification and fraud solutions. Socure’s data science environment includes a streaming pipeline called Transaction ETL (TETL), built on OSS Apache Spark running on Amazon EKS. TETL ingests and processes data volumes ranging from small to large datasets while maintaining high-throughput performance. In this post, we show how Socure was able to achieve 50% cost reduction by migrating the TETL streaming pipeline from self-managed spark to Amazon EMR serverless.

This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.

In this post, we analyze the results from our benchmark tests comparing the Amazon EMR 7.9 optimized Spark runtime against Spark 3.5.5 without encryption optimizations. We walk through a detailed cost analysis and provide step-by-step instructions to reproduce the benchmark.

In this post, we demonstrate the write performance benefits of using the Amazon EMR 7.12 runtime for Spark and Iceberg compares to open source Spark 3.5.6 with Iceberg 1.10.0 tables on a 3TB merge workload.

In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services.