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	<title>Amazon EMR &#8211; Noise</title>
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		<title>Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables</title>
		<link>https://noise.getoto.net/2025/12/02/amazon-s3-storage-lens-adds-performance-metrics-support-for-billions-of-prefixes-and-export-to-s3-tables/</link>
		
		<dc:creator><![CDATA[Veliswa Boya]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 16:15:12 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[storage]]></category>
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					<description><![CDATA[New capabilities help optimize application performance, analyze unlimited prefixes, and simplify metrics analysis through S3 Tables integration.]]></description>
		
		
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			</item>
		<item>
		<title>Run Apache Spark and Iceberg 4.5x faster than open source Spark with Amazon EMR</title>
		<link>https://noise.getoto.net/2025/11/27/run-apache-spark-and-iceberg-4-5x-faster-than-open-source-spark-with-amazon-emr/</link>
		
		<dc:creator><![CDATA[Atul Payapilly]]></dc:creator>
		<pubDate>Thu, 27 Nov 2025 01:46:19 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.]]></description>
		
		
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			</item>
		<item>
		<title>Apache Spark encryption performance improvement with Amazon EMR 7.9</title>
		<link>https://noise.getoto.net/2025/11/27/apache-spark-encryption-performance-improvement-with-amazon-emr-7-9/</link>
		
		<dc:creator><![CDATA[Sonu Kumar Singh]]></dc:creator>
		<pubDate>Thu, 27 Nov 2025 01:37:55 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Run Apache Spark and Apache Iceberg write jobs 2x faster with Amazon EMR</title>
		<link>https://noise.getoto.net/2025/11/27/run-apache-spark-and-apache-iceberg-write-jobs-2x-faster-with-amazon-emr/</link>
		
		<dc:creator><![CDATA[Atul Payapilly]]></dc:creator>
		<pubDate>Thu, 27 Nov 2025 01:03:08 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerate data lake operations with Apache Iceberg V3 deletion vectors and row lineage</title>
		<link>https://noise.getoto.net/2025/11/27/accelerate-data-lake-operations-with-apache-iceberg-v3-deletion-vectors-and-row-lineage/</link>
		
		<dc:creator><![CDATA[Ron Ortloff]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 22:05:47 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/10/13/visualize-data-lineage-using-amazon-sagemaker-catalog-for-amazon-emr-aws-glue-and-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Shubham Purwar]]></dc:creator>
		<pubDate>Mon, 13 Oct 2025 19:08:49 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=89e754efcbf447ec16867fdb98ac995b</guid>

					<description><![CDATA[Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including […]]]></description>
		
		
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			</item>
		<item>
		<title>Optimize Amazon EMR runtime for Apache Spark with EMR S3A</title>
		<link>https://noise.getoto.net/2025/09/24/optimize-amazon-emr-runtime-for-apache-spark-with-emr-s3a/</link>
		
		<dc:creator><![CDATA[Giovanni Matteo Fumarola]]></dc:creator>
		<pubDate>Wed, 24 Sep 2025 20:51:44 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7548a7ed3f9f32c96fabe688441490c1</guid>

					<description><![CDATA[With the Amazon EMR 7.10 runtime, Amazon EMR has introduced EMR S3A, an improved implementation of the open source S3A file system connector. In this post, we showcase the enhanced read and write performance advantages of using Amazon EMR 7.10.0 runtime for Apache Spark with EMR S3A as compared to EMRFS and the open source S3A file system connector.]]></description>
		
		
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			</item>
		<item>
		<title>Unlock the power of Apache Iceberg v3 deletion vectors on Amazon EMR</title>
		<link>https://noise.getoto.net/2025/09/17/unlock-the-power-of-apache-iceberg-v3-deletion-vectors-on-amazon-emr/</link>
		
		<dc:creator><![CDATA[Arun Shanmugam]]></dc:creator>
		<pubDate>Wed, 17 Sep 2025 19:26:40 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c1f463952c733693d8a98668982d95b9</guid>

					<description><![CDATA[As modern data architectures expand, Apache Iceberg has become a widely popular open table format, providing ACID transactions, time travel, and schema evolution. In table format v2, Iceberg introduced merge-on-read, improving delete and update handling through positional delete files. These files improve write performance but can slow down reads when not compacted, since Iceberg must […]]]></description>
		
		
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			</item>
		<item>
		<title>Automate and orchestrate Amazon EMR jobs using AWS Step Functions and Amazon EventBridge</title>
		<link>https://noise.getoto.net/2025/09/15/automate-and-orchestrate-amazon-emr-jobs-using-aws-step-functions-and-amazon-eventbridge/</link>
		
		<dc:creator><![CDATA[Senthil Kamala Rathinam]]></dc:creator>
		<pubDate>Mon, 15 Sep 2025 17:10:24 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7dc214b11296b36a10da44579d014a90</guid>

					<description><![CDATA[In this post, we discuss how to build a fully automated, scheduled Spark processing pipeline using Amazon EMR on EC2, orchestrated with Step Functions and triggered by EventBridge. We walk through how to deploy this solution using AWS CloudFormation, processes COVID-19 public dataset data in Amazon Simple Storage Service (Amazon S3), and store the aggregated results in Amazon S3.]]></description>
		
		
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			</item>
		<item>
		<title>Streamline Spark application development on Amazon EMR with the Data Solutions Framework on AWS</title>
		<link>https://noise.getoto.net/2025/09/15/streamline-spark-application-development-on-amazon-emr-with-the-data-solutions-framework-on-aws/</link>
		
		<dc:creator><![CDATA[Vincent Gromakowski]]></dc:creator>
		<pubDate>Mon, 15 Sep 2025 17:05:43 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Cloud Development Kit]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Developer Tools]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=3d22de1555061b8076e14b76ea0351c3</guid>

					<description><![CDATA[In this post, we explore how to use Amazon EMR, the AWS Cloud Development Kit (AWS CDK), and the Data Solutions Framework (DSF) on AWS to streamline the development process, from setting up a local development environment to deploying serverless Spark infrastructure, and implementing a CI/CD pipeline for automated testing and deployment.]]></description>
		
		
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			</item>
		<item>
		<title>Deploy Apache YuniKorn batch scheduler for Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2025/09/02/deploy-apache-yunikorn-batch-scheduler-for-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Suvojit Dasgupta]]></dc:creator>
		<pubDate>Tue, 02 Sep 2025 20:22:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=497f5421c7c54a816a5ac129af43680b</guid>

					<description><![CDATA[This post explores Kubernetes scheduling fundamentals, examines the limitations of the default kube-scheduler for batch workloads, and demonstrates how YuniKorn addresses these challenges. We discuss how to deploy YuniKorn as a custom scheduler for Amazon EMR on EKS, its integration with job submissions, how to configure queues and placement rules, and how to establish resource quotas. We also show these features in action through practical Spark job examples.]]></description>
		
		
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			</item>
		<item>
		<title>How Ancestry optimizes a 100-billion-row Iceberg table</title>
		<link>https://noise.getoto.net/2025/08/28/how-ancestry-optimizes-a-100-billion-row-iceberg-table/</link>
		
		<dc:creator><![CDATA[Thomas Cardenas]]></dc:creator>
		<pubDate>Wed, 27 Aug 2025 23:20:30 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2b5ca7c84b93066175eb814c5b605874</guid>

					<description><![CDATA[This is a guest post by Thomas Cardenas, Staff Software Engineer at Ancestry, in partnership with AWS. Ancestry, the global leader in family history and consumer genomics, uses family trees, historical records, and DNA to help people on their journeys of personal discovery. Ancestry has the largest collection of family history records, consisting of 40 […]]]></description>
		
		
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			</item>
		<item>
		<title>Improve Amazon EMR HBase availability and tail latency using generational ZGC</title>
		<link>https://noise.getoto.net/2025/08/19/improve-amazon-emr-hbase-availability-and-tail-latency-using-generational-zgc/</link>
		
		<dc:creator><![CDATA[Vishal Chaudhary]]></dc:creator>
		<pubDate>Tue, 19 Aug 2025 17:21:56 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[HBase]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f66e54202e55f89cec57aebbef7dcc5b</guid>

					<description><![CDATA[Large-scale HBase deployments on Amazon EMR suffer from unpredictable garbage collection behavior that creates performance bottlenecks for business-critical applications. To solve this problem, Amazon EMR leverages Oracle's generational ZGC technology from JDK 21 to deliver predictable, sub-millisecond pause times. This post shows you how to configure generational ZGC in Amazon EMR 7.10.0, apply performance tuning methods, and optimize HBase RegionServer garbage collection settings.]]></description>
		
		
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			</item>
		<item>
		<title>Achieve low-latency data processing with Amazon EMR on AWS Local Zones</title>
		<link>https://noise.getoto.net/2025/08/18/achieve-low-latency-data-processing-with-amazon-emr-on-aws-local-zones/</link>
		
		<dc:creator><![CDATA[Gagan Brahmi]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 18:56:57 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=bbca86aebe16d591c041621cd884f00c</guid>

					<description><![CDATA[By deploying Amazon EMR on AWS Local Zones, organizations can achieve single-digit millisecond latency data processing for applications while maintaining data residency compliance. This post demonstrates how to use AWS Local Zones to deploy EMR clusters closer to your users, enabling millisecond-level response times.]]></description>
		
		
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		<item>
		<title>Enhance Amazon EMR observability with automated incident mitigation using Amazon Bedrock and Amazon Managed Grafana</title>
		<link>https://noise.getoto.net/2025/08/14/enhance-amazon-emr-observability-with-automated-incident-mitigation-using-amazon-bedrock-and-amazon-managed-grafana/</link>
		
		<dc:creator><![CDATA[Yu-Ting Su]]></dc:creator>
		<pubDate>Thu, 14 Aug 2025 15:25:16 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Bedrock Agents]]></category>
		<category><![CDATA[Amazon Bedrock Knowledge Bases]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon Managed Grafana]]></category>
		<category><![CDATA[Amazon Managed Service for Prometheus]]></category>
		<category><![CDATA[Amazon Nova]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we demonstrate how to integrate real-time monitoring with AI-powered remediation suggestions, combining Amazon Managed Grafana for visualization, Amazon Bedrock for intelligent response recommendations, and AWS Systems Manager for automated remediation actions on Amazon Web Services (AWS).]]></description>
		
		
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		<item>
		<title>Use Databricks Unity Catalog Open APIs for Spark workloads on Amazon EMR</title>
		<link>https://noise.getoto.net/2025/07/25/use-databricks-unity-catalog-open-apis-for-spark-workloads-on-amazon-emr/</link>
		
		<dc:creator><![CDATA[Venkat Viswanathan]]></dc:creator>
		<pubDate>Fri, 25 Jul 2025 16:18:02 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Data Catalog]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c6fca50e433255d676fb98c77a8bf105</guid>

					<description><![CDATA[In this post, we demonstrate the powerful interoperability between Amazon EMR and Databricks Unity Catalog by walking through how to enable external access to Unity Catalog, configure EMR Spark to connect seamlessly with Unity Catalog, and perform DML and DDL operations on Unity Catalog tables using EMR Serverless.]]></description>
		
		
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		<item>
		<title>RocksDB 101: Optimizing stateful streaming in Apache Spark with Amazon EMR and AWS Glue</title>
		<link>https://noise.getoto.net/2025/06/18/rocksdb-101-optimizing-stateful-streaming-in-apache-spark-with-amazon-emr-and-aws-glue/</link>
		
		<dc:creator><![CDATA[Melody Yang]]></dc:creator>
		<pubDate>Wed, 18 Jun 2025 19:28:04 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[This post explores RocksDB's key features and demonstrates its implementation using Spark on Amazon EMR and AWS Glue, providing you with the knowledge you need to scale your real-time data processing capabilities.]]></description>
		
		
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			</item>
		<item>
		<title>Using AWS Glue Data Catalog views with Apache Spark in EMR Serverless and Glue 5.0</title>
		<link>https://noise.getoto.net/2025/06/05/using-aws-glue-data-catalog-views-with-apache-spark-in-emr-serverless-and-glue-5-0/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 05 Jun 2025 16:45:46 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4dc3225699218a5cc7c935d60886a438</guid>

					<description><![CDATA[In this post, we guide you through the process of creating a Data Catalog view using EMR Serverless, adding the SQL dialect to the view for Athena, sharing it with another account using LF-Tags, and then querying the view in the recipient account using a separate EMR Serverless workspace and AWS Glue 5.0 Spark job and Athena. This demonstration showcases the versatility and cross-account capabilities of Data Catalog views and access through various AWS analytics services.]]></description>
		
		
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		<item>
		<title>Build a centralized observability platform for Apache Spark on Amazon EMR on EKS using external Spark History Server</title>
		<link>https://noise.getoto.net/2025/06/03/build-a-centralized-observability-platform-for-apache-spark-on-amazon-emr-on-eks-using-external-spark-history-server/</link>
		
		<dc:creator><![CDATA[Sri Potluri]]></dc:creator>
		<pubDate>Tue, 03 Jun 2025 16:20:37 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c31ae3b162b5f425b837208e6ffffb74</guid>

					<description><![CDATA[This post demonstrates how to build a centralized observability platform using SHS for Spark applications running on EMR on EKS. We showcase how to enhance SHS with performance monitoring tools, with a pattern applicable to many monitoring solutions such as SparkMeasure and DataFlint.]]></description>
		
		
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			</item>
		<item>
		<title>Build a secure serverless streaming pipeline with Amazon MSK Serverless, Amazon EMR Serverless and IAM</title>
		<link>https://noise.getoto.net/2025/06/02/build-a-secure-serverless-streaming-pipeline-with-amazon-msk-serverless-amazon-emr-serverless-and-iam/</link>
		
		<dc:creator><![CDATA[Shubham Purwar]]></dc:creator>
		<pubDate>Mon, 02 Jun 2025 14:45:04 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
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					<description><![CDATA[The post demonstrates a comprehensive, end-to-end solution for processing data from MSK Serverless using an EMR Serverless Spark Streaming job, secured with IAM authentication. Additionally, it demonstrates how to query the processed data using Amazon Athena, providing a seamless and integrated workflow for data processing and analysis. This solution enables near real-time querying of the latest data processed from MSK Serverless and EMR Serverless using Athena, providing instant insights and analytics.]]></description>
		
		
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