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	<title>Amazon EMR on EKS &#8211; Noise</title>
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		<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>
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					<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>
		<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>
		<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>
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					<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>
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					<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>
		<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>
		<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>
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					<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>
		<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>
		<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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		<item>
		<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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7e7b8f0f0d937677fa0a4c2660ed9b28</guid>

					<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>
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					<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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		<item>
		<title>Cost monitoring for Amazon EMR on Amazon EKS</title>
		<link>https://noise.getoto.net/2023/06/09/cost-monitoring-for-amazon-emr-on-amazon-eks/</link>
		
		<dc:creator><![CDATA[Lotfi Mouhib]]></dc:creator>
		<pubDate>Fri, 09 Jun 2023 17:17:11 +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[Amazon EMR is the industry-leading cloud big data solution, providing a collection of open-source frameworks such as Spark, Hive, Hudi, and Presto, fully managed and with per-second billing. Amazon EMR on Amazon EKS is a deployment option allowing you to deploy Amazon EMR on the same Amazon Elastic Kubernetes Service (Amazon EKS) clusters that is […]]]></description>
		
		
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		<title>Introducing Amazon EMR on EKS job submission with Spark Operator and spark-submit</title>
		<link>https://noise.getoto.net/2023/06/06/introducing-amazon-emr-on-eks-job-submission-with-spark-operator-and-spark-submit/</link>
		
		<dc:creator><![CDATA[Lotfi Mouhib]]></dc:creator>
		<pubDate>Tue, 06 Jun 2023 19:29:04 +0000</pubDate>
				<category><![CDATA[#emroneks #ec2spot #costoptimization #sparkec2spot]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<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[Apache Spark]]></category>
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					<description><![CDATA[Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With EMR on EKS, Spark applications run on the Amazon EMR runtime for Apache Spark. This performance-optimized runtime offered by Amazon EMR makes your Spark jobs run fast […]]]></description>
		
		
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		<title>Improve reliability and reduce costs of your Apache Spark workloads with vertical autoscaling on Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2023/05/04/improve-reliability-and-reduce-costs-of-your-apache-spark-workloads-with-vertical-autoscaling-on-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Rajkishan Gunasekaran]]></dc:creator>
		<pubDate>Thu, 04 May 2023 18:38:35 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Analytics]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=333e79f8b20230799b974217a333cb70</guid>

					<description><![CDATA[Amazon EMR on Amazon EKS is a deployment option offered by Amazon EMR that enables you to run Apache Spark applications on Amazon Elastic Kubernetes Service (Amazon EKS) in a cost-effective manner. It uses the EMR runtime for Apache Spark to increase performance so that your jobs run faster and cost less. Apache Spark allows […]]]></description>
		
		
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		<title>Amazon EMR on EKS widens the performance gap: Run Apache Spark workloads 5.37 times faster and at 4.3 times lower cost</title>
		<link>https://noise.getoto.net/2023/04/12/amazon-emr-on-eks-widens-the-performance-gap-run-apache-spark-workloads-5-37-times-faster-and-at-4-3-times-lower-cost/</link>
		
		<dc:creator><![CDATA[Melody Yang]]></dc:creator>
		<pubDate>Wed, 12 Apr 2023 19:27:03 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=d1ce34fd477f93d18194137fd93e8f7a</guid>

					<description><![CDATA[Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With EMR on EKS, Spark applications run on the Amazon EMR runtime for Apache Spark. This performance-optimized runtime offered by Amazon EMR makes your Spark jobs run fast […]]]></description>
		
		
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		<title>Build event-driven data pipelines using AWS Controllers for Kubernetes and Amazon EMR on EKS</title>
		<link>https://noise.getoto.net/2023/03/30/build-event-driven-data-pipelines-using-aws-controllers-for-kubernetes-and-amazon-emr-on-eks/</link>
		
		<dc:creator><![CDATA[Victor Gu]]></dc:creator>
		<pubDate>Thu, 30 Mar 2023 18:20:50 +0000</pubDate>
				<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=48bd8760bdca0fe68eaf9b8ae704db09</guid>

					<description><![CDATA[An event-driven architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events via an event broker. By promoting loose coupling between components of a system, an event-driven architecture leads to greater agility and can enable components in the system to scale independently and fail without impacting other services. […]]]></description>
		
		
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		<item>
		<title>How SafeGraph built a reliable, efficient, and user-friendly Apache Spark platform with Amazon EMR on Amazon EKS</title>
		<link>https://noise.getoto.net/2023/02/21/how-safegraph-built-a-reliable-efficient-and-user-friendly-apache-spark-platform-with-amazon-emr-on-amazon-eks/</link>
		
		<dc:creator><![CDATA[Nan Zhu]]></dc:creator>
		<pubDate>Tue, 21 Feb 2023 16:15:42 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ac58e9490a5cf5bb44b0b7f9321e5ad4</guid>

					<description><![CDATA[This is a guest post by Nan Zhu, Engineering Manager/Software Engineer, SafeGraph, and Dave Thibault, Sr. Solutions Architect – AWS SafeGraph is a geospatial data company that curates over 41 million global points of interest (POIs) with detailed attributes, such as brand affiliation, advanced category tagging, and open hours, as well as how people interact […]]]></description>
		
		
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