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	<title>Analytics &#8211; Noise</title>
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		<title>Announcing replication support and Intelligent-Tiering for Amazon S3 Tables</title>
		<link>https://noise.getoto.net/2025/12/02/announcing-replication-support-and-intelligent-tiering-for-amazon-s3-tables/</link>
		
		<dc:creator><![CDATA[Sébastien Stormacq]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 16:19:14 +0000</pubDate>
				<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
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					<description><![CDATA[New features enable automatic cost optimization through intelligent storage tiering and simplified table replication across AWS Regions and accounts.]]></description>
		
		
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		<item>
		<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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9698a1afbacdc6a3437013a179f15565</guid>

					<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>Amazon OpenSearch Service improves vector database performance and cost with GPU acceleration and auto-optimization</title>
		<link>https://noise.getoto.net/2025/12/02/amazon-opensearch-service-improves-vector-database-performance-and-cost-with-gpu-acceleration-and-auto-optimization/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 16:06:41 +0000</pubDate>
				<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ecf93b82c3f2ada7221f448e46c45e6e</guid>

					<description><![CDATA[Build and optimize large-scale vector databases up to 10 times faster and at a quarter of the cost with new GPU acceleration and auto-optimization capabilities that automatically balance search quality, speed, and resource usage.]]></description>
		
		
		<enclosure url="" length="0" type="" />

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		<item>
		<title>Amazon FSx for NetApp ONTAP now integrates with Amazon S3 for seamless data access</title>
		<link>https://noise.getoto.net/2025/12/02/amazon-fsx-for-netapp-ontap-now-integrates-with-amazon-s3-for-seamless-data-access/</link>
		
		<dc:creator><![CDATA[Veliswa Boya]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 15:59:54 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock Knowledge Bases]]></category>
		<category><![CDATA[Amazon FSx for NetApp ONTAP]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Compute]]></category>
		<category><![CDATA[serverless]]></category>
		<category><![CDATA[storage]]></category>
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					<description><![CDATA[Access FSx for NetApp ONTAP file data through S3 to enable AI/ML workloads and analytics—letting you use enterprise file data with Bedrock, SageMaker, and analytics services while it remains in your file system.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>AWS Clean Rooms launches privacy-enhancing synthetic dataset generation for ML model training</title>
		<link>https://noise.getoto.net/2025/12/01/aws-clean-rooms-launches-privacy-enhancing-synthetic-dataset-generation-for-ml-model-training/</link>
		
		<dc:creator><![CDATA[Micah Walter]]></dc:creator>
		<pubDate>Mon, 01 Dec 2025 01:55:54 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Clean Rooms]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=03c073487cc3e446d0675c3dc7c98e4a</guid>

					<description><![CDATA[Train ML models on sensitive collaborative data by generating synthetic datasets that preserve statistical patterns while protecting individual privacy through configurable noise levels and protection against re-identification.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e32b6e434dd068f299d42422a83d5b83</guid>

					<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>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Medidata’s journey to a modern lakehouse architecture on AWS</title>
		<link>https://noise.getoto.net/2025/11/27/medidatas-journey-to-a-modern-lakehouse-architecture-on-aws/</link>
		
		<dc:creator><![CDATA[Mike Araujo]]></dc:creator>
		<pubDate>Thu, 27 Nov 2025 01:00:46 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Case Study]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>New one-click onboarding and notebooks with a built-in AI agent in Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2025/11/22/new-one-click-onboarding-and-notebooks-with-a-built-in-ai-agent-in-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Sat, 22 Nov 2025 01:23:12 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=b971fcd64aa48c504908181a1e5f5e80</guid>

					<description><![CDATA[Amazon SageMaker Unified Studio introduces new one-click onboarding experiences and serverless notebooks with a built-in AI agent without any manual set up or provisioning of your domain or compute resources. You can launch SageMaker Unified Studio directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, and Amazon S3 Tables console pages, giving a fast path to analytics and AI workloads.]]></description>
		
		
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			</item>
		<item>
		<title>New business metadata features in Amazon SageMaker Catalog to improve discoverability across organizations</title>
		<link>https://noise.getoto.net/2025/11/19/new-business-metadata-features-in-amazon-sagemaker-catalog-to-improve-discoverability-across-organizations/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Wed, 19 Nov 2025 19:09:27 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
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					<description><![CDATA[Amazon SageMaker Catalog now offers column-level metadata forms and enforced glossary requirements, enabling organizations to improve data classification, discoverability, and governance through standardized business metadata.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Your guide to AWS Analytics at AWS re:Invent 2025</title>
		<link>https://noise.getoto.net/2025/11/13/your-guide-to-aws-analytics-at-aws-reinvent-2025/</link>
		
		<dc:creator><![CDATA[Sonu Kumar Singh]]></dc:creator>
		<pubDate>Thu, 13 Nov 2025 20:06:19 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[Events]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a54ac28b18099afc57bec26c144337eb</guid>

					<description><![CDATA[It’s that time of year again — AWS re:Invent is here! At re:Invent, bold ideas come to life. Get a front-row seat to hear inspiring stories from AWS experts, customers, and leaders as they explore today’s most impactful topics, from data analytics to AI. For all the data enthusiasts and professionals, we’ve curated a comprehensive […]]]></description>
		
		
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		<item>
		<title>Scaling data governance with Amazon DataZone: Covestro success story</title>
		<link>https://noise.getoto.net/2025/11/03/scaling-data-governance-with-amazon-datazone-covestro-success-story/</link>
		
		<dc:creator><![CDATA[Jörg Janssen]]></dc:creator>
		<pubDate>Mon, 03 Nov 2025 21:02:06 +0000</pubDate>
				<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1f2181b18e9d7d33980dfdcf7921e2b2</guid>

					<description><![CDATA[In this post, we show you how Covestro transformed its data architecture by implementing Amazon DataZone and AWS Serverless Data Lake Framework, transitioning from a centralized data lake to a data mesh architecture. The implementation enabled streamlined data access, better data quality, and stronger governance at scale, achieving a 70% reduction in time-to-market for over 1,000 data pipelines.]]></description>
		
		
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			</item>
		<item>
		<title>Unlock real-time data insights with schema evolution using Amazon MSK Serverless, Iceberg, and AWS Glue streaming</title>
		<link>https://noise.getoto.net/2025/10/23/unlock-real-time-data-insights-with-schema-evolution-using-amazon-msk-serverless-iceberg-and-aws-glue-streaming/</link>
		
		<dc:creator><![CDATA[Nitin Kumar]]></dc:creator>
		<pubDate>Thu, 23 Oct 2025 19:54:50 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4455f93bbac72fada532e45bf5b58465</guid>

					<description><![CDATA[This post showcases a solution that businesses can use to access real-time data insights without the traditional delays between data creation and analysis. By combining Amazon MSK Serverless, Debezium MySQL connector, AWS Glue streaming, and Apache Iceberg tables, the architecture captures database changes instantly and makes them immediately available for analytics through Amazon Athena. A standout feature is the system's ability to automatically adapt when database structures change—such as adding new columns—without disrupting operations or requiring manual intervention.]]></description>
		
		
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		<item>
		<title>Stifel’s approach to scalable Data Pipeline Orchestration in Data Mesh</title>
		<link>https://noise.getoto.net/2025/10/22/stifels-approach-to-scalable-data-pipeline-orchestration-in-data-mesh/</link>
		
		<dc:creator><![CDATA[Srinivas Kandi, Hossein Johari, Ahmad Rawashdeh, Lei Meng]]></dc:creator>
		<pubDate>Tue, 21 Oct 2025 21:02:00 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=132b926f661baca17cc6f31626e32123</guid>

					<description><![CDATA[Stifel Financial Corp, a diversified financial services holding company is expanding its data landscape that requires an orchestration solution capable of managing increasingly complex data pipeline operations across multiple business domains. Traditional time-based scheduling systems fall short in addressing the dynamic interdependencies between data products, requires event-driven orchestration. Key challenges include coordinating cross-domain dependencies, maintaining data consistency across business units, meeting stringent SLAs, and scaling effectively as data volumes grow. Without a flexible orchestration solution, these issues can lead to delayed business operations and insights, increased operational overhead, and heightened compliance risks due to manual interventions and rigid scheduling mechanisms that cannot adapt to evolving business needs. In this post, we walk through how Stifel Financial Corp, in collaboration with AWS ProServe, has addressed these challenges by building a modular, event-driven orchestration solution using AWS native services that enables precise triggering of data pipelines based on dependency satisfaction, supporting near real-time responsiveness and cross-domain coordination.]]></description>
		
		
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		<item>
		<title>How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto</title>
		<link>https://noise.getoto.net/2025/10/21/how-twilio-built-a-multi-engine-query-platform-using-amazon-athena-and-open-source-presto/</link>
		
		<dc:creator><![CDATA[Amber Runnels]]></dc:creator>
		<pubDate>Tue, 21 Oct 2025 20:57:32 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Presto]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=0e2ce42226e35576856ac1ad4cd68d72</guid>

					<description><![CDATA[At Twilio, we manage a 20 petabyte-scale Amazon S3 data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution.]]></description>
		
		
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		<item>
		<title>Best practices for upgrading from Amazon Redshift DC2 to RA3 and Amazon Redshift Serverless</title>
		<link>https://noise.getoto.net/2025/10/16/best-practices-for-upgrading-from-amazon-redshift-dc2-to-ra3-and-amazon-redshift-serverless/</link>
		
		<dc:creator><![CDATA[Ziad Wali]]></dc:creator>
		<pubDate>Wed, 15 Oct 2025 21:35:48 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f2b039d7a1d645c22a288668b817a9c3</guid>

					<description><![CDATA[As analytical demands grow, many customers are upgrading from DC2 to RA3 or Amazon Redshift Serverless, which offer independent compute and storage scaling, along with advanced capabilities such as data sharing, zero-ETL integration, and built-in artificial intelligence and machine learning (AI/ML) support with Amazon Redshift ML. This post provides a practical guide to plan your target architecture and migration strategy, covering upgrade options, key considerations, and best practices to facilitate a successful and seamless transition.]]></description>
		
		
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		<item>
		<title>Building a real-time ICU patient analytics pipeline with AWS Lambda event source mapping</title>
		<link>https://noise.getoto.net/2025/10/11/building-a-real-time-icu-patient-analytics-pipeline-with-aws-lambda-event-source-mapping/</link>
		
		<dc:creator><![CDATA[Priyanka Chaudhary]]></dc:creator>
		<pubDate>Fri, 10 Oct 2025 21:55:53 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS IoT Core]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=273a5c09548b2a3a6a834ded0d63b2a1</guid>

					<description><![CDATA[In this post, we demonstrate how to build a serverless architecture that processes real-time ICU patient monitoring data using Lambda event source mapping for immediate alert generation and data aggregation, followed by persistent storage in Amazon S3 with an Iceberg catalog for comprehensive healthcare analytics.]]></description>
		
		
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		<item>
		<title>Bridging data silos: cross-bounded context querying with Vanguard’s Operational Read-only Data Store (ORDS) using Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/10/07/bridging-data-silos-cross-bounded-context-querying-with-vanguards-operational-read-only-data-store-ords-using-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Naresh Rajaram]]></dc:creator>
		<pubDate>Mon, 06 Oct 2025 21:32:33 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4624d6280c36765e28bb6ba76a0e7518</guid>

					<description><![CDATA[At Vanguard, we faced significant challenges with our legacy mainframe system that limited our ability to deliver modern, personalized customer experiences. Our centralized database architecture created performance bottlenecks and made it difficult to scale services independently for our millions of personal and institutional investors. In this post, we show you how we modernized our data architecture using Amazon Redshift as our Operational Read-only Data Store (ORDS).]]></description>
		
		
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		<item>
		<title>How Laravel Nightwatch handles billions of observability events in real time with Amazon MSK and ClickHouse Cloud</title>
		<link>https://noise.getoto.net/2025/10/01/how-laravel-nightwatch-handles-billions-of-observability-events-in-real-time-with-amazon-msk-and-clickhouse-cloud/</link>
		
		<dc:creator><![CDATA[Masudur Rahaman Sayem]]></dc:creator>
		<pubDate>Wed, 01 Oct 2025 16:59:49 +0000</pubDate>
				<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Laravel, one of the world’s most popular web frameworks, launched its first-party observability platform, Laravel Nightwatch, to provide developers with real-time insights into application performance. Built entirely on AWS managed services and ClickHouse Cloud, the service already processes over one billion events per day while maintaining sub-second query latency, giving developers instant visibility into the health of their applications.]]></description>
		
		
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		<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>
		<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>
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					<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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