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	<title>Amazon EMR on EKS &#8211; Noise</title>
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		<title>Announcing Spark Connect on Amazon EMR on EKS: Interactive PySpark development, anywhere</title>
		<link>https://noise.getoto.net/2026/09/29/announcing-spark-connect-on-amazon-emr-on-eks-interactive-pyspark-development-anywhere/</link>
		
		<dc:creator><![CDATA[Amit Maindola]]></dc:creator>
		<pubDate>Tue, 29 Sep 2026 16:12:17 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[announcements]]></category>
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					<description><![CDATA[Announcing Spark Connect on Amazon EMR on EKS: build, test, and debug Spark applications from VS Code, PyCharm, Jupyter notebooks, Amazon SageMaker Unified Studio, or dbt, while running full-scale Spark operations on your existing Amazon EKS clusters.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing Apache Spark troubleshooting agent for Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2026/08/03/introducing-apache-spark-troubleshooting-agent-for-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Vara Bonthu]]></dc:creator>
		<pubDate>Mon, 03 Aug 2026 16:12:00 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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		<item>
		<title>High-performance Remote Shuffle Service on Amazon EMR with Apache Celeborn</title>
		<link>https://noise.getoto.net/2026/07/15/high-performance-remote-shuffle-service-on-amazon-emr-with-apache-celeborn/</link>
		
		<dc:creator><![CDATA[Suvojit Dasgupta]]></dc:creator>
		<pubDate>Wed, 15 Jul 2026 15:56:25 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>AWS Weekly Roundup: Anthropic &#038; Meta partnership, AWS Lambda S3 Files, Amazon Bedrock AgentCore CLI, and more (April 27, 2026)</title>
		<link>https://noise.getoto.net/2026/04/27/aws-weekly-roundup-anthropic-meta-partnership-aws-lambda-s3-files-amazon-bedrock-agentcore-cli-and-more-april-27-2026/</link>
		
		<dc:creator><![CDATA[Daniel Abib]]></dc:creator>
		<pubDate>Mon, 27 Apr 2026 15:01:28 +0000</pubDate>
				<category><![CDATA[Amazon Aurora]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Bedrock AgentCore]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[news]]></category>
		<category><![CDATA[Week in Review]]></category>
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					<description><![CDATA[Late March took me to Seattle for the Specialist Tech Conference, one of the most energizing gatherings of AWS specialists from around the world. It was an incredible opportunity to connect with peers, exchange experiences, and go deep on the latest advancements in Generative AI and Amazon Bedrock — and a powerful reminder of something […]]]></description>
		
		
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			</item>
		<item>
		<title>Implementing Kerberos authentication for Apache Spark jobs on Amazon EMR on EKS to access a Kerberos-enabled Hive Metastore</title>
		<link>https://noise.getoto.net/2026/04/13/implementing-kerberos-authentication-for-apache-spark-jobs-on-amazon-emr-on-eks-to-access-a-kerberos-enabled-hive-metastore/</link>
		
		<dc:creator><![CDATA[Krishna Kumar Venkateswaran]]></dc:creator>
		<pubDate>Mon, 13 Apr 2026 15:51:31 +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[Technical How-to]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>How Razorpay achieved 11% performance improvement and 21% cost reduction with Amazon EMR</title>
		<link>https://noise.getoto.net/2026/03/09/how-razorpay-achieved-11-performance-improvement-and-21-cost-reduction-with-amazon-emr/</link>
		
		<dc:creator><![CDATA[Narendra Kumar]]></dc:creator>
		<pubDate>Mon, 09 Mar 2026 16:40:35 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Financial Services]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f5ca742e99b223f3c7238ebfd6e79455</guid>

					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Implement fine-grained access control for Iceberg tables using Amazon EMR on EKS integrated with AWS Lake Formation</title>
		<link>https://noise.getoto.net/2025/10/24/implement-fine-grained-access-control-for-iceberg-tables-using-amazon-emr-on-eks-integrated-with-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Tejal Patel]]></dc:creator>
		<pubDate>Fri, 24 Oct 2025 20:39:25 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=476b706e38da34c4c074e7eaafd1b212</guid>

					<description><![CDATA[On February 6th 2025, AWS introduced fine-grained access control based on AWS Lake Formation for EMR on EKS from Amazon EMR 7.7 and higher version. You can now significantly enhance your data governance and security frameworks using this feature. In this post, we demonstrate how to implement FGAC on Apache Iceberg tables using EMR on EKS with Lake Formation.]]></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>
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					<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>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>
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					<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>
		<title>Enhance AI-assisted development with Amazon ECS, Amazon EKS and AWS Serverless MCP server</title>
		<link>https://noise.getoto.net/2025/05/29/enhance-ai-assisted-development-with-amazon-ecs-amazon-eks-and-aws-serverless-mcp-server/</link>
		
		<dc:creator><![CDATA[Elizabeth Fuentes]]></dc:creator>
		<pubDate>Thu, 29 May 2025 19:11:49 +0000</pubDate>
				<category><![CDATA[Amazon API Gateway]]></category>
		<category><![CDATA[Amazon Elastic Container Registry]]></category>
		<category><![CDATA[Amazon Elastic Container Service]]></category>
		<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon Q]]></category>
		<category><![CDATA[Amazon Q Developer]]></category>
		<category><![CDATA[Application Services*]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Compute]]></category>
		<category><![CDATA[Containers]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4b4b555c621c02a8339a4c0bb1151391</guid>

					<description><![CDATA[AWS introduces specialized Model Context Protocol (MCP) servers for Amazon ECS, EKS, Finch and AWS Serverless, providing real-time contextual responses and service-specific guidance to guide AI assisted application development.]]></description>
		
		
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			</item>
		<item>
		<title>Build end-to-end Apache Spark pipelines with Amazon MWAA, Batch Processing Gateway, and Amazon EMR on EKS clusters</title>
		<link>https://noise.getoto.net/2025/05/01/build-end-to-end-apache-spark-pipelines-with-amazon-mwaa-batch-processing-gateway-and-amazon-emr-on-eks-clusters/</link>
		
		<dc:creator><![CDATA[Avinash Desireddy]]></dc:creator>
		<pubDate>Thu, 01 May 2025 15:51:29 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[open source]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=caf1e550ef43020d339c155278b57566</guid>

					<description><![CDATA[This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.]]></description>
		
		
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			</item>
		<item>
		<title>Design patterns for implementing Hive Metastore for Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2025/02/28/design-patterns-for-implementing-hive-metastore-for-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Avinash Desireddy]]></dc:creator>
		<pubDate>Fri, 28 Feb 2025 18:58:12 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we explore the design patterns for implementing the Hive Metastore (HMS) with EMR on EKS with Spark Operator, each offering distinct advantages depending on your requirements. Whether you choose to deploy HMS as a sidecar container within the Apache Spark Driver pod, or as a Kubernetes deployment in the data processing EKS cluster, or as an external HMS service in a separate EKS cluster, the key considerations revolve around communication efficiency, scalability, resource isolation, high availability, and security.]]></description>
		
		
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		<item>
		<title>How Nielsen uses serverless concepts on Amazon EKS for big data processing with Spark workloads</title>
		<link>https://noise.getoto.net/2025/01/28/how-nielsen-uses-serverless-concepts-on-amazon-eks-for-big-data-processing-with-spark-workloads/</link>
		
		<dc:creator><![CDATA[Shani Adadi Kazaz]]></dc:creator>
		<pubDate>Tue, 28 Jan 2025 16:37:22 +0000</pubDate>
				<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Amazon Simple Queue Service (SQS)]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Experience-Based Acceleration]]></category>
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					<description><![CDATA[In this post, we follow Nielsen’s journey to build a robust and scalable architecture while enjoying linear scaling. We start by examining the initial challenges Nielsen faced and the root causes behind these issues. Then, we explore Nielsen’s solution: running Spark on Amazon Elastic Kubernetes Service (Amazon EKS) while adopting serverless concepts.]]></description>
		
		
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		<item>
		<title>Build a high-performance quant research platform with Apache Iceberg</title>
		<link>https://noise.getoto.net/2025/01/09/build-a-high-performance-quant-research-platform-with-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Guy Bachar]]></dc:creator>
		<pubDate>Thu, 09 Jan 2025 20:55:39 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[EKS]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=233ad968db2737c62263cd3357ac05de</guid>

					<description><![CDATA[In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg, we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct, and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity.]]></description>
		
		
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		<item>
		<title>Use Batch Processing Gateway to automate job management in multi-cluster Amazon EMR on EKS environments</title>
		<link>https://noise.getoto.net/2024/09/13/use-batch-processing-gateway-to-automate-job-management-in-multi-cluster-amazon-emr-on-eks-environments/</link>
		
		<dc:creator><![CDATA[Umair Nawaz]]></dc:creator>
		<pubDate>Fri, 13 Sep 2024 18:51:11 +0000</pubDate>
				<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=16777eae0c14766e6c6aa326f1f00aea</guid>

					<description><![CDATA[AWS customers often process petabytes of data using Amazon EMR on EKS. In enterprise environments with diverse workloads or varying operational requirements, customers frequently choose a multi-cluster setup due to the following advantages: Better resiliency and no single point of failure – If one cluster fails, other clusters can continue processing critical workloads, maintaining business […]]]></description>
		
		
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		<item>
		<title>Introducing Amazon EMR on EKS with Apache Flink: A scalable, reliable, and efficient data processing platform</title>
		<link>https://noise.getoto.net/2024/05/28/introducing-amazon-emr-on-eks-with-apache-flink-a-scalable-reliable-and-efficient-data-processing-platform/</link>
		
		<dc:creator><![CDATA[Kinnar Kumar Sen]]></dc:creator>
		<pubDate>Tue, 28 May 2024 17:07:19 +0000</pubDate>
				<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Compute]]></category>
		<category><![CDATA[Containers]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=26e62e995a29a4b407fbaf8902800379</guid>

					<description><![CDATA[AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]]]></description>
		
		
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		<item>
		<title>Dive deep into security management: The Data on EKS Platform</title>
		<link>https://noise.getoto.net/2024/04/29/dive-deep-into-security-management-the-data-on-eks-platform/</link>
		
		<dc:creator><![CDATA[Yuzhu Xiao]]></dc:creator>
		<pubDate>Mon, 29 Apr 2024 15:36:04 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a12085dc62faa2831d9ca6254f693609</guid>

					<description><![CDATA[The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS, an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS). In the realm of big data, securing […]]]></description>
		
		
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		<title>Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2023/10/18/run-apache-hive-workloads-using-spark-sql-with-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Amit Maindola]]></dc:creator>
		<pubDate>Wed, 18 Oct 2023 16:07:10 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Using Spark SQL to run Hive workloads provides not only the simplicity of SQL-like queries but also taps into the exceptional speed and performance provided by Spark. Spark SQL is an Apache Spark module for structured data processing. One […]]]></description>
		
		
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		<item>
		<title>Mixing AWS Graviton with x86 CPUs to optimize cost and resiliency using Amazon EKS</title>
		<link>https://noise.getoto.net/2023/07/13/mixing-aws-graviton-with-x86-cpus-to-optimize-cost-and-resiliency-using-amazon-eks/</link>
		
		<dc:creator><![CDATA[Macey Neff]]></dc:creator>
		<pubDate>Thu, 13 Jul 2023 20:56:21 +0000</pubDate>
				<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon EKS]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[AWS Graviton]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Graviton]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=206fbdbb063757a3968edb4bf312098a</guid>

					<description><![CDATA[This post is written by Yahav Biran, Principal SA, and Yuval Dovrat, Israel Head Compute SA. This post shows you how to integrate AWS Graviton-based Amazon EC2 instances into an existing Amazon Elastic Kubernetes Service (Amazon EKS) environment running on x86-based Amazon EC2 instances. Customers use mixed-CPU architectures to enable their application to utilize a wide selection […]]]></description>
		
		
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		<item>
		<title>Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg</title>
		<link>https://noise.getoto.net/2023/07/03/backtesting-index-rebalancing-arbitrage-with-amazon-emr-and-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Guy Bachar]]></dc:creator>
		<pubDate>Mon, 03 Jul 2023 18:14:39 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=842ae13184e55da5dfa3f9d7c32f90b0</guid>

					<description><![CDATA[Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Index rebalancing arbitrage takes advantage of short-term price discrepancies resulting from ETF managers’ efforts to […]]]></description>
		
		
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