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	<title>Amazon EMR &#8211; Noise</title>
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		<title>Bring data quality to your Amazon EMR workloads with Amazon EMR Data Quality built on AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2026/10/13/bring-data-quality-to-your-amazon-emr-workloads-with-amazon-emr-data-quality-built-on-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Mert Hocanin]]></dc:creator>
		<pubDate>Mon, 12 Oct 2026 23:40:26 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[AWS Glue Data Quality is now available natively on Amazon EMR, bundled in AWS runtime for Apache Spark (emr-spark-8.1.0). Write the same DQDL rulesets you use in AWS Glue directly in your Amazon EMR Spark jobs. This post walks through two patterns: lightweight inline validation and an Apache Iceberg Write-Audit-Publish workflow with managed rulesets.]]></description>
		
		
		
			</item>
		<item>
		<title>Query across accounts and table formats with multi-catalog in Amazon EMR</title>
		<link>https://noise.getoto.net/2026/10/06/query-across-accounts-and-table-formats-with-multi-catalog-in-amazon-emr/</link>
		
		<dc:creator><![CDATA[Suthan Phillips]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 16:05:27 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
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					<description><![CDATA[Amazon EMR 8.1.0 adds multi-catalog support through the RedirectingSessionCatalog, so you can query Iceberg, Delta Lake, Hudi, and Hive tables through a single catalog and join data across AWS accounts without copying it. This post shows how to put these capabilities into practice on Amazon EMR Serverless.]]></description>
		
		
		
			</item>
		<item>
		<title>Announcing Spark Connect on Amazon EMR on EC2: Interactive PySpark anywhere</title>
		<link>https://noise.getoto.net/2026/09/23/announcing-spark-connect-on-amazon-emr-on-ec2-interactive-pyspark-anywhere/</link>
		
		<dc:creator><![CDATA[Al MS]]></dc:creator>
		<pubDate>Wed, 23 Sep 2026 18:28:32 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
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					<description><![CDATA[Amazon EMR on EC2 now supports Spark Connect, so you can develop and debug PySpark interactively from Amazon SageMaker Unified Studio Data Notebooks or your own IDE while Spark runs on your cluster. This post shows you how to get started from both a Data Notebook and a local IDE.]]></description>
		
		
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		<item>
		<title>Accelerating Spark queries with Iceberg materialized views</title>
		<link>https://noise.getoto.net/2026/09/10/accelerating-spark-queries-with-iceberg-materialized-views/</link>
		
		<dc:creator><![CDATA[Yuzhou Sun]]></dc:creator>
		<pubDate>Thu, 10 Sep 2026 16:05:45 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Accelerate slow, repetitive Apache Spark analytical queries on Apache Iceberg tables without rewriting any SQL. This post shows how automatic query rewrite in Amazon EMR and AWS Glue uses Iceberg materialized views in the AWS Glue Data Catalog to transparently substitute matching query plans, and how to design materialized views for the best speedup.]]></description>
		
		
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			</item>
		<item>
		<title>How Moovit achieved 33% cost optimization through architectural modernization</title>
		<link>https://noise.getoto.net/2026/09/03/how-moovit-achieved-33-cost-optimization-through-architectural-modernization/</link>
		
		<dc:creator><![CDATA[Saar Porat]]></dc:creator>
		<pubDate>Thu, 03 Sep 2026 16:23:46 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[Learn how Moovit modernized its data platform with a multi-engine lakehouse architecture: offloading heavy aggregation workloads from Amazon Redshift to Amazon EMR with Spark SQL, isolating workloads with Amazon Redshift Serverless, and cutting overall data pipeline cost by 33%.]]></description>
		
		
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			</item>
		<item>
		<title>Query Amazon S3 Tables from Amazon EMR Trino using the Iceberg REST endpoint</title>
		<link>https://noise.getoto.net/2026/09/02/query-amazon-s3-tables-from-amazon-emr-trino-using-the-iceberg-rest-endpoint/</link>
		
		<dc:creator><![CDATA[Shubham Purwar]]></dc:creator>
		<pubDate>Wed, 02 Sep 2026 19:01:30 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Learn how to query Amazon S3 Tables from Trino on Amazon EMR using the Apache Iceberg REST catalog endpoint. This post shows how to deploy the integration with AWS CloudFormation, configure the Trino catalog, and run SQL to create, query, and manage Apache Iceberg tables.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerate Apache Spark debugging on Amazon EMR with AWS DevOps Agent</title>
		<link>https://noise.getoto.net/2026/09/01/accelerate-apache-spark-debugging-on-amazon-emr-with-aws-devops-agent/</link>
		
		<dc:creator><![CDATA[Kalyan Janaki]]></dc:creator>
		<pubDate>Tue, 01 Sep 2026 16:06:44 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Extend AWS DevOps Agent to investigate Apache Spark failures on Amazon EMR. This post shows how to register the Apache Spark Troubleshooting Agent for Amazon EMR as a custom MCP capability provider over AWS PrivateLink, so a single agent chat session diagnoses a failing Spark job from an Amazon CloudWatch alarm to a line-numbered root cause.]]></description>
		
		
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			</item>
		<item>
		<title>GPU-accelerated Apache Spark with Amazon EMR and NVIDIA RTX PRO 4500 on Amazon EC2 G7 instances runs up to 3.7x faster</title>
		<link>https://noise.getoto.net/2026/08/25/gpu-accelerated-apache-spark-with-amazon-emr-and-nvidia-rtx-pro-4500-on-amazon-ec2-g7-instances-runs-up-to-3-7x-faster/</link>
		
		<dc:creator><![CDATA[McCall Peltier]]></dc:creator>
		<pubDate>Tue, 25 Aug 2026 16:17:45 +0000</pubDate>
				<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Amazon EMR on EKS now runs Apache Spark up to 3.7x faster on Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell GPUs than on comparable CPU instances, with no changes to existing Spark code. See the TPC-DS benchmark results, the cost comparison, and how to get started.]]></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>
		<item>
		<title>Lowering AWS KMS decrypt API costs in EMR Spark jobs</title>
		<link>https://noise.getoto.net/2026/07/30/lowering-aws-kms-decrypt-api-costs-in-emr-spark-jobs/</link>
		
		<dc:creator><![CDATA[Navaneedha Krishnan Jagathesan]]></dc:creator>
		<pubDate>Thu, 30 Jul 2026 16:12:41 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Processing encrypted data in Amazon S3 with Amazon EMR and Apache Spark can drive up AWS KMS decrypt API costs as the number of objects grows. This post shows three techniques to reduce those costs without compromising encryption: optimizing file formats (including Apache Iceberg), aggregating data, and using AWS Glue Data Catalog partition indexes.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerate Spark on EMR Serverless with larger workers and shuffle-optimized disks</title>
		<link>https://noise.getoto.net/2026/07/29/accelerate-spark-on-emr-serverless-with-larger-workers-and-shuffle-optimized-disks/</link>
		
		<dc:creator><![CDATA[Karthik Prabhakar]]></dc:creator>
		<pubDate>Wed, 29 Jul 2026 18:51:12 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Amazon EMR Serverless now supports a 32 vCPU / 244 GB worker configuration for the most demanding Spark jobs. Across 126 TPC-DS and TPC-H queries, larger workers delivered an average 29% faster query execution and 29% lower cost, with the biggest gains on shuffle-heavy, multi-table join queries.]]></description>
		
		
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			</item>
		<item>
		<title>Automate Spark Scala migration to 4.x with AWS Spark Upgrade Agent</title>
		<link>https://noise.getoto.net/2026/07/28/automate-spark-scala-migration-to-4-x-with-aws-spark-upgrade-agent/</link>
		
		<dc:creator><![CDATA[Bezuayehu Wate]]></dc:creator>
		<pubDate>Tue, 28 Jul 2026 17:30:07 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Learn how to automate Apache Spark 3.x to 4.0 Scala migration on Amazon EMR using the AWS Spark Upgrade Agent. This post covers API deprecations, behavioral changes, build configuration updates, and job validation, turning months of manual effort into hours.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How Mapfre USA modernized fraud claims with Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2026/07/15/how-mapfre-usa-modernized-fraud-claims-with-amazon-emr-serverless-2/</link>
		
		<dc:creator><![CDATA[Lijan Kuniyil]]></dc:creator>
		<pubDate>Tue, 14 Jul 2026 21:47:35 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[AWS Fargate]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Secrets Manager]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e1b3a39ce6eb74cb6dc1da07482b9c92</guid>

					<description><![CDATA[Insurance fraud remains a significant challenge for the insurance industry because fraudulent claims can increase loss costs, reduce trust, and consume investigation capacity that could otherwise be focused on serving customers. Traditional fraud detection approaches typically rely on rules-based controls, manual investigation triggers, historical claim patterns, and structured-data-only analysis. These approaches are useful for known […]]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Multi-cloud lakehouse architecture on AWS for Agentic AI, Part 1: Architecture and best practices</title>
		<link>https://noise.getoto.net/2026/07/13/multi-cloud-lakehouse-architecture-on-aws-for-agentic-ai-part-1-architecture-and-best-practices/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Mon, 13 Jul 2026 17:02:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Bedrock AgentCore]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a03b3dc738f79d55acf66a71db609d95</guid>

					<description><![CDATA[This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How MAPFRE USA modernized fraud claims with Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2026/07/13/how-mapfre-usa-modernized-fraud-claims-with-amazon-emr-serverless/</link>
		
		<dc:creator><![CDATA[Lijan Kuniyil]]></dc:creator>
		<pubDate>Mon, 13 Jul 2026 09:26:59 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[generative AI]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=189d601872bea283915cea88c972a2f0</guid>

					<description><![CDATA[Insurance fraud remains a significant challenge for the insurance industry. Fraudulent claims can increase loss costs, reduce trust, and consume investigation capacity that could otherwise be focused on serving customers. Traditional fraud detection approaches typically rely on rules-based controls, manual investigation triggers, historical claim patterns, and structured-data-only analysis. These approaches are useful for known fraud […]]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How BigBasket uses the Iceberg based lakehouse architecture on AWS to power lightning-fast grocery delivery across India</title>
		<link>https://noise.getoto.net/2026/07/06/how-bigbasket-uses-the-iceberg-based-lakehouse-architecture-on-aws-to-power-lightning-fast-grocery-delivery-across-india/</link>
		
		<dc:creator><![CDATA[Annie Mattoo]]></dc:creator>
		<pubDate>Mon, 06 Jul 2026 16:50:17 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Quick Sight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a58db9aae671ef573b0e4a36a50351a0</guid>

					<description><![CDATA[In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.]]></description>
		
		
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			</item>
		<item>
		<title>Deploy modern data platforms in minutes with MDAA</title>
		<link>https://noise.getoto.net/2026/07/02/deploy-modern-data-platforms-in-minutes-with-mdaa/</link>
		
		<dc:creator><![CDATA[Sudeshna Dash]]></dc:creator>
		<pubDate>Thu, 02 Jul 2026 16:26:28 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS CloudTrail]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Key Management Service*]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Partner solutions]]></category>
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					<description><![CDATA[In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.]]></description>
		
		
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			</item>
		<item>
		<title>Modernizing financial analytics with Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2026/06/22/modernizing-financial-analytics-with-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Umang Aggarwal]]></dc:creator>
		<pubDate>Mon, 22 Jun 2026 16:42:55 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS IAM Identity Center]]></category>
		<category><![CDATA[AWS Identity and Access Management (IAM)]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=afd764b970053657691acc95e0a86c02</guid>

					<description><![CDATA[Avanse Financial Services, India’s leading education loan providers, migrated to a cloud-native lakehouse architecture using Amazon SageMaker Unified Studio, which unified their data engineering, analytics, and artificial intelligence (AI) workflows in a single governed environment on AWS. In this post, we walk through their migration journey so you can adapt their approach to your own environment.]]></description>
		
		
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		<item>
		<title>Upgrade PySpark from Spark 3.5 to Spark 4.0 with AWS Spark Upgrade Agent</title>
		<link>https://noise.getoto.net/2026/06/09/upgrade-pyspark-from-spark-3-5-to-spark-4-0-with-aws-spark-upgrade-agent/</link>
		
		<dc:creator><![CDATA[Prasad Nadig]]></dc:creator>
		<pubDate>Tue, 09 Jun 2026 16:17:23 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we walk through a hands-on PySpark migration from Spark 3.5 to Spark 4.0 on Amazon EMR Serverless, using the AWS Spark Upgrade Agent. You’ll see how the agent iteratively validates your application on a live Amazon EMR Serverless application, automatically diagnosing and resolving failures from Amazon CloudWatch logs until the job succeeds.]]></description>
		
		
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		<item>
		<title>Announcing Spark Connect on Amazon EMR Serverless: Interactive PySpark development, anywhere</title>
		<link>https://noise.getoto.net/2026/06/09/announcing-spark-connect-on-amazon-emr-serverless-interactive-pyspark-development-anywhere/</link>
		
		<dc:creator><![CDATA[Al MS]]></dc:creator>
		<pubDate>Tue, 09 Jun 2026 16:15:54 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[serverless]]></category>
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					<description><![CDATA[Today, AWS is announcing support for Spark Connect on Amazon EMR Serverless with EMR release 7.13 (Apache Spark 3.5.6) and later versions. You can now build and debug Spark applications from your preferred local environment while running full-scale Spark operations on EMR Serverless.]]></description>
		
		
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