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	<title>Amazon Redshift &#8211; Noise</title>
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		<title>Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables</title>
		<link>https://noise.getoto.net/2025/12/02/amazon-s3-storage-lens-adds-performance-metrics-support-for-billions-of-prefixes-and-export-to-s3-tables/</link>
		
		<dc:creator><![CDATA[Veliswa Boya]]></dc:creator>
		<pubDate>Tue, 02 Dec 2025 16:15:12 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[storage]]></category>
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					<description><![CDATA[New capabilities help optimize application performance, analyze unlimited prefixes, and simplify metrics analysis through S3 Tables integration.]]></description>
		
		
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			</item>
		<item>
		<title>Achieve 2x faster data lake query performance with Apache Iceberg on Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/11/27/achieve-2x-faster-data-lake-query-performance-with-apache-iceberg-on-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Kalaiselvi Kamaraj]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 22:16:15 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[In 2025, Amazon Redshift delivered several performance optimizations that improved query performance over twofold for Iceberg workloads on Amazon Redshift Serverless, delivering exceptional performance and cost-effectiveness for your data lake workloads. In this post, we describe some of the optimizations that led to these performance gains.]]></description>
		
		
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		<item>
		<title>Getting started with Apache Iceberg write support in Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/11/26/getting-started-with-apache-iceberg-write-support-in-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Sanket Hase]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 19:34:55 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.]]></description>
		
		
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			</item>
		<item>
		<title>Save up to 24% on Amazon Redshift Serverless compute costs with Reservations</title>
		<link>https://noise.getoto.net/2025/11/25/save-up-to-24-on-amazon-redshift-serverless-compute-costs-with-reservations/</link>
		
		<dc:creator><![CDATA[Satesh Sonti]]></dc:creator>
		<pubDate>Mon, 24 Nov 2025 22:14:23 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Cloud Cost Optimization]]></category>
		<category><![CDATA[serverless]]></category>
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					<description><![CDATA[In this post, you learn how Amazon Redshift Serverless Reservations can help you lower your data warehouse costs. We explore ways to determine the optimal number of RPUs to reserve, review example scenarios, and discuss important considerations when purchasing these reservations.]]></description>
		
		
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			</item>
		<item>
		<title>Upgrade from Amazon Redshift DC2 node type to Amazon Redshift Serverless</title>
		<link>https://noise.getoto.net/2025/10/23/upgrade-from-amazon-redshift-dc2-node-type-to-amazon-redshift-serverless/</link>
		
		<dc:creator><![CDATA[Nita Shah]]></dc:creator>
		<pubDate>Wed, 22 Oct 2025 21:04:31 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we show you the upgrade process from DC2 instances to Amazon Redshift Serverless. By using Amazon Redshift Serverless, you can run and scale analytics without managing data warehouse infrastructure.]]></description>
		
		
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			</item>
		<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>
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					<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>
		<item>
		<title>Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/10/13/visualize-data-lineage-using-amazon-sagemaker-catalog-for-amazon-emr-aws-glue-and-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Shubham Purwar]]></dc:creator>
		<pubDate>Mon, 13 Oct 2025 19:08:49 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=89e754efcbf447ec16867fdb98ac995b</guid>

					<description><![CDATA[Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including […]]]></description>
		
		
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			</item>
		<item>
		<title>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>
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					<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>
		<item>
		<title>Accelerating SQL analytics with Amazon Redshift MCP server</title>
		<link>https://noise.getoto.net/2025/09/24/accelerating-sql-analytics-with-amazon-redshift-mcp-server/</link>
		
		<dc:creator><![CDATA[Ramkumar Nottath]]></dc:creator>
		<pubDate>Tue, 23 Sep 2025 21:04:01 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we walk through setting up the Amazon Redshift MCP server and demonstrate how a data analyst can efficiently explore Redshift data warehouses and perform data analysis using natural language queries.]]></description>
		
		
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			</item>
		<item>
		<title>Integrate Tableau and PingFederate with Amazon Redshift using AWS IAM Identity Center</title>
		<link>https://noise.getoto.net/2025/09/19/integrate-tableau-and-pingfederate-with-amazon-redshift-using-aws-iam-identity-center/</link>
		
		<dc:creator><![CDATA[Rohit Vashishtha]]></dc:creator>
		<pubDate>Thu, 18 Sep 2025 22:02:45 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we outline a comprehensive guide for setting up single sign-on from Tableau desktop to Amazon Redshift using integration with IAM Identity Center and PingFederate as the identity provider (IdP) with an LDAP based data store, AWS Directory Service for Microsoft Active Directory.]]></description>
		
		
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			</item>
		<item>
		<title>Modernize Amazon Redshift authentication by migrating user management to AWS IAM Identity Center</title>
		<link>https://noise.getoto.net/2025/08/28/modernize-amazon-redshift-authentication-by-migrating-user-management-to-aws-iam-identity-center/</link>
		
		<dc:creator><![CDATA[Ziad Wali]]></dc:creator>
		<pubDate>Wed, 27 Aug 2025 23:26:51 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS IAM Identity Center]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=5dd59952639cd852b90c77e10a6c1a80</guid>

					<description><![CDATA[Amazon Redshift is a powerful cloud-based data warehouse that organizations can use to analyze both structured and semi-structured data through advanced SQL queries. As a fully managed service, it provides high performance and scalability while allowing secure access to the data stored in the data warehouse. Organizations worldwide rely on Amazon Redshift to handle massive […]]]></description>
		
		
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			</item>
		<item>
		<title>Zero-ETL: How AWS is tackling data integration challenges</title>
		<link>https://noise.getoto.net/2025/08/26/zero-etl-how-aws-is-tackling-data-integration-challenges/</link>
		
		<dc:creator><![CDATA[Nikki Rouda]]></dc:creator>
		<pubDate>Tue, 26 Aug 2025 18:40:25 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9238405eba2b468d52cb6fecd833c7af</guid>

					<description><![CDATA[In this blog post, we show you how Amazon Web Services (AWS) is simplifying data integration with zero-ETL while realizing performance benefits and cost optimizations. As organizations gather data for analytics and AI, they are increasingly finding themselves caught in a complex web of extract, transform, and load (ETL) pipelines—the traditional backbone of data integration. […]]]></description>
		
		
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		<item>
		<title>Best practices for migrating Teradata BTEQ scripts to Amazon Redshift RSQL</title>
		<link>https://noise.getoto.net/2025/08/25/best-practices-for-migrating-teradata-bteq-scripts-to-amazon-redshift-rsql/</link>
		
		<dc:creator><![CDATA[Ankur Bhanawat]]></dc:creator>
		<pubDate>Mon, 25 Aug 2025 17:24:07 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Migration]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7405b56f2aed519b675fbac8b19f2d91</guid>

					<description><![CDATA[When migrating from Teradata BTEQ (Basic Teradata Query) to Amazon Redshift RSQL, following established best practices helps ensure maintainable, efficient, and reliable code. While the AWS Schema Conversion Tool (AWS SCT) automatically handles the basic conversion of BTEQ scripts to RSQL, it primarily focuses on SQL syntax translation and basic script conversion. However, to achieve […]]]></description>
		
		
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			</item>
		<item>
		<title>Amazon Redshift Serverless at 4 RPUs: High-value analytics at low cost</title>
		<link>https://noise.getoto.net/2025/08/22/amazon-redshift-serverless-at-4-rpus-high-value-analytics-at-low-cost/</link>
		
		<dc:creator><![CDATA[Ricardo Serafim]]></dc:creator>
		<pubDate>Fri, 22 Aug 2025 20:46:01 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Redshift Serverless]]></category>
		<category><![CDATA[serverless]]></category>
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					<description><![CDATA[Amazon Redshift Serverless now supports 4 RPU configurations, helping you get started with a lower base capacity that runs scalable analytics workloads beginning at $1.50 per hour. In this post, we examine how this new sizing option makes Redshift Serverless accessible to smaller organizations while providing enterprises with cost-effective environments for development, testing, and variable workloads.]]></description>
		
		
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		<item>
		<title>Build data pipelines with dbt in Amazon Redshift using Amazon MWAA and Cosmos</title>
		<link>https://noise.getoto.net/2025/08/13/build-data-pipelines-with-dbt-in-amazon-redshift-using-amazon-mwaa-and-cosmos/</link>
		
		<dc:creator><![CDATA[Cindy Li]]></dc:creator>
		<pubDate>Wed, 13 Aug 2025 20:12:37 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we explore a streamlined, configuration-driven approach to orchestrate dbt Core jobs using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Cosmos, an open source package. These jobs run transformations on Amazon Redshift. With this setup, teams can collaborate effectively while maintaining data quality, operational efficiency, and observability.]]></description>
		
		
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		<item>
		<title>Near real-time streaming analytics on protobuf with Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/08/04/near-real-time-streaming-analytics-on-protobuf-with-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Konstantinos Tzouvanas]]></dc:creator>
		<pubDate>Mon, 04 Aug 2025 17:06:42 +0000</pubDate>
				<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we explore an end-to-end analytics workload for streaming protobuf data, by showcasing how to handle these data streams with Amazon Redshift Streaming Ingestion, deserializing and processing them using AWS Lambda functions, so that the incoming streams are immediately available for querying and analytical processing on Amazon Redshift.]]></description>
		
		
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		<item>
		<title>Amazon Redshift out-of-the-box performance innovations for data lake queries</title>
		<link>https://noise.getoto.net/2025/07/31/amazon-redshift-out-of-the-box-performance-innovations-for-data-lake-queries/</link>
		
		<dc:creator><![CDATA[Martin Milenkoski]]></dc:creator>
		<pubDate>Thu, 31 Jul 2025 14:05:10 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Expert (400)]]></category>
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					<description><![CDATA[In this post, we first briefly review how planner statistics are collected and what impact they have on queries. Then, we discuss Amazon Redshift features that deliver optimal plans on Iceberg tables and Parquet data even with the lack of statistics. Finally, we review some example queries that now execute faster because of these latest Amazon Redshift innovations.]]></description>
		
		
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		<item>
		<title>Secure generative SQL with Amazon Q</title>
		<link>https://noise.getoto.net/2025/07/25/secure-generative-sql-with-amazon-q/</link>
		
		<dc:creator><![CDATA[Gregory Knowles]]></dc:creator>
		<pubDate>Fri, 25 Jul 2025 16:25:20 +0000</pubDate>
				<category><![CDATA[Amazon Q]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Q/Machine Learning.]]></category>
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					<description><![CDATA[In this post, we discuss the design and security controls in place when using generative SQL and its use in both Amazon SageMaker Unified Studio and Amazon Redshift Query Editor v2.]]></description>
		
		
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		<item>
		<title>Revenue NSW modernises analytics with AWS, enabling unified and scalable data management, processing, and access</title>
		<link>https://noise.getoto.net/2025/07/15/revenue-nsw-modernises-analytics-with-aws-enabling-unified-and-scalable-data-management-processing-and-access/</link>
		
		<dc:creator><![CDATA[Saeed Barghi]]></dc:creator>
		<pubDate>Tue, 15 Jul 2025 12:04:48 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Database Migration Service]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[Revenue NSW, Australia's principal revenue management agency, successfully modernized its analytics infrastructure using AWS services. In this blog post, we show how the organization transformed its on-premises data environment into a unified, scalable cloud-based solution using Amazon Redshift, AWS Database Migration Service, Amazon AppFlow, and AWS Glue.]]></description>
		
		
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		<title>Harnessing the Power of Nested Materialized Views and exploring Cascading Refresh</title>
		<link>https://noise.getoto.net/2025/07/11/harnessing-the-power-of-nested-materialized-views-and-exploring-cascading-refresh/</link>
		
		<dc:creator><![CDATA[Ritesh Sinha]]></dc:creator>
		<pubDate>Fri, 11 Jul 2025 15:46:01 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[database]]></category>
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					<description><![CDATA[In this post, we explore how to maximize Amazon Redshift query performance through nested materialized views and implementing cascading refresh strategies. We demonstrate how to create materialized views based on other materialized views, enabling a hierarchical structure of precomputed results that significantly enhances query performance and data processing efficiency, particularly useful for reusing precomputed joins with different aggregate options.]]></description>
		
		
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