Category: Amazon EMR

In this post, we walk you through five key enhancements: Amazon CloudWatch Logs integration, step-level Amazon Simple Storage Service (Amazon S3) logging controls, expanded console UIs for YARN and Tez, Amazon EMR step to YARN application ID mapping, and enhanced custom metrics with updated documentation.

In this post, we show you how to build an AI-powered troubleshooting solution using Amazon OpenSearch Service vector search and intelligent analysis. This solution reduces HBase inconsistency resolution from hours to minutes and root cause identification from days to hours through natural language queries over operational data. This democratizes HBase troubleshooting capabilities across teams and reducing dependency on specialized expertise.

In this post, we walk through how to set up and manage S3 Tables in the AWS Glue Data Catalog, create and query Iceberg materialized views, and configure access controls that work across your analytics stack with IAM-based authorization.

In this post, we show how to configure Kerberos authentication for Spark jobs on Amazon EMR on EKS, authenticating against a Kerberos-enabled HMS so you can run both Amazon EMR on EC2 and Amazon EMR on EKS workloads against a single, secure HMS deployment.

Growing data volume, variety, and velocity has made it crucial for businesses to implement architectures that efficiently manage and analyze data, while maintaining data integrity and consistency. In this post, we show you a solution that combines Apache Iceberg, Data Build Tool (dbt), and Amazon EMR to create a scalable, ACID-compliant transactional data lake. You can use this data lake to process transactions and analyze data simultaneously while maintaining data accuracy and real-time insights for better decision-making.

In this post, we explore the cost improvements we observed when benchmarking Apache Spark jobs with serverless storage on EMR Serverless. We take a deeper look at how serverless storage helps reduce costs for shuffle-heavy Spark workloads, and we outline practical guidance on identifying the types of queries that can benefit most from enabling serverless storage in your EMR Serverless Spark jobs.

In this post, we demonstrate how to improve HBase read performance by implementing bucket caching on Amazon EMR. Our tests reduced latency by 57.9% and improved throughput by 138.8%. This solution is particularly valuable for large-scale HBase deployments on Amazon S3 that need to optimize read performance while managing costs.

In this post, we explore how Razorpay, India’s leading FinTech company, transformed their data platform by migrating from a third-party solution to Amazon EMR, unlocking improved performance and significant cost savings. We’ll walk through the architectural decisions that guided this migration, the implementation strategy, and the measurable benefits Razorpay achieved.

This post explores how to build and manage a comprehensive extract, transform, and load (ETL) pipeline using SageMaker Unified Studio workflows through a code-based approach. We demonstrate how to use a single, integrated interface to handle all aspects of data processing, from preparation to orchestration, by using AWS services including Amazon EMR, AWS Glue, Amazon Redshift, and Amazon MWAA. This solution streamlines the data pipeline through a single UI.

In this post, we show you how to implement real-time data correlation using Apache Flink to join streaming order data with historical customer and product information, enabling you to make informed decisions based on comprehensive, up-to-date analytics. We also introduce an optimized solution to automatically load Hive dimension table data into Alluxio Universal Flash Storage (UFS) through the Alluxio cache layer. This enables Flink to perform temporal joins on changing data, accurately reflecting the content of a table at specific points in time.