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	<title>AWS Glue &#8211; Noise</title>
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		<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>
		
		
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		<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>
		
		
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			</item>
		<item>
		<title>Introducing catalog federation for Apache Iceberg tables in the AWS Glue Data Catalog</title>
		<link>https://noise.getoto.net/2025/11/27/introducing-catalog-federation-for-apache-iceberg-tables-in-the-aws-glue-data-catalog/</link>
		
		<dc:creator><![CDATA[Debika D]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 22:08:07 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a4f3c3ef6b6f67ced039fddb217a4a4f</guid>

					<description><![CDATA[AWS Glue now supports catalog federation for remote Iceberg tables in the Data Catalog. With catalog federation, you can query remote Iceberg tables, stored in Amazon S3 and cataloged in remote Iceberg catalogs, using AWS analytics engines and without moving or duplicating tables. In this post, we discuss how to get started with catalog federation for Iceberg tables in the Data Catalog.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerate data lake operations with Apache Iceberg V3 deletion vectors and row lineage</title>
		<link>https://noise.getoto.net/2025/11/27/accelerate-data-lake-operations-with-apache-iceberg-v3-deletion-vectors-and-row-lineage/</link>
		
		<dc:creator><![CDATA[Ron Ortloff]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 22:05:47 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services.]]></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>
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					<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>
		<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>
		<title>Seamlessly Integrate Data on Google BigQuery and ClickHouse Cloud with AWS Glue</title>
		<link>https://noise.getoto.net/2025/10/07/seamlessly-integrate-data-on-google-bigquery-and-clickhouse-cloud-with-aws-glue/</link>
		
		<dc:creator><![CDATA[Ray Wang]]></dc:creator>
		<pubDate>Mon, 06 Oct 2025 21:29:33 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Migrating from Google Cloud’s BigQuery to ClickHouse Cloud on AWS allows businesses to leverage the speed and efficiency of ClickHouse for real-time analytics while benefiting from AWS’s scalable and secure environment. This article provides a comprehensive guide to executing a direct data migration using AWS Glue ETL, highlighting the advantages and best practices for a […]]]></description>
		
		
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			</item>
		<item>
		<title>Break down data silos and seamlessly query Iceberg tables in Amazon SageMaker from Snowflake</title>
		<link>https://noise.getoto.net/2025/09/15/break-down-data-silos-and-seamlessly-query-iceberg-tables-in-amazon-sagemaker-from-snowflake/</link>
		
		<dc:creator><![CDATA[Nidhi Gupta]]></dc:creator>
		<pubDate>Mon, 15 Sep 2025 20:12:22 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Partner solutions]]></category>
		<category><![CDATA[S3 Select]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7e176ccfbe7fee30fd88822fd355a915</guid>

					<description><![CDATA[This blog post discusses how to create a seamless integration between Amazon SageMaker Lakehouse and Snowflake for modern data analytics. It specifically demonstrates how organizations can enable Snowflake to access tables in AWS Glue Data Catalog (stored in S3 buckets) through SageMaker Lakehouse Iceberg REST Catalog, with security managed by AWS Lake Formation. The post provides a detailed technical walkthrough of implementing this integration, including creating IAM roles and policies, configuring Lake Formation access controls, setting up catalog integration in Snowflake, and managing data access permissions. While four different patterns exist for accessing Iceberg tables from Snowflake, the blog focuses on the first pattern using catalog integration with SigV4 authentication and Lake Formation credential vending.]]></description>
		
		
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		<item>
		<title>Accelerate AWS Glue Zero-ETL data ingestion using Salesforce Bulk API</title>
		<link>https://noise.getoto.net/2025/09/08/accelerate-aws-glue-zero-etl-data-ingestion-using-salesforce-bulk-api/</link>
		
		<dc:creator><![CDATA[Shashank Sharma]]></dc:creator>
		<pubDate>Mon, 08 Sep 2025 18:29:41 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<category><![CDATA[zero-ETL]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=47c7037125f87ea1230a16bc6dc3d07e</guid>

					<description><![CDATA[AWS Glue Zero ETL (extract, transform, and load) now supports Salesforce Bulk API, delivering substantial performance gains compared to Salesforce REST API for large-scale data integration for targets such as Amazon SageMaker lakehouse and Amazon Redshift. In this blog post, we show you how to use Zero-ETL powered by AWS Glue with Salesforce Bulk API to accelerate your data integration processes.]]></description>
		
		
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		<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>The Amazon SageMaker lakehouse architecture now automates optimization configuration of Apache Iceberg tables on Amazon S3</title>
		<link>https://noise.getoto.net/2025/08/09/the-amazon-sagemaker-lakehouse-architecture-now-automates-optimization-configuration-of-apache-iceberg-tables-on-amazon-s3/</link>
		
		<dc:creator><![CDATA[Tomohiro Tanaka]]></dc:creator>
		<pubDate>Fri, 08 Aug 2025 21:40:34 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e551e1dfdb2e8fbbab8bf577301f8165</guid>

					<description><![CDATA[The Amazon SageMaker lakehouse architecture now automates optimization of Iceberg tables stored in Amazon S3 with catalog-level configuration, optimizing storage in your Iceberg tables and improving query performance. This post demonstrates an end-to-end flow to enable catalog level table optimization setting.]]></description>
		
		
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		<item>
		<title>Automate data lineage in Amazon SageMaker using AWS Glue Crawlers supported data sources</title>
		<link>https://noise.getoto.net/2025/07/30/automate-data-lineage-in-amazon-sagemaker-using-aws-glue-crawlers-supported-data-sources/</link>
		
		<dc:creator><![CDATA[Mohit Dawar]]></dc:creator>
		<pubDate>Wed, 30 Jul 2025 16:33:20 +0000</pubDate>
				<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Crawlers]]></category>
		<category><![CDATA[Data Lineage]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we explore its real-world impact through the lens of an ecommerce company striving to boost their bottom line. To illustrate this practical application, we walk you through how you can use the prebuilt integration between SageMaker Catalog and AWS Glue crawlers to automatically capture lineage for data assets stored in Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.]]></description>
		
		
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		<item>
		<title>Accelerate your data quality journey for lakehouse architecture with Amazon SageMaker, Apache Iceberg on AWS, Amazon S3 tables, and AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2025/07/28/accelerate-your-data-quality-journey-for-lakehouse-architecture-with-amazon-sagemaker-apache-iceberg-on-aws-amazon-s3-tables-and-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Brody Pearman]]></dc:creator>
		<pubDate>Mon, 28 Jul 2025 18:09:37 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
		<category><![CDATA[data governance]]></category>
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					<description><![CDATA[This post explores how you can use AWS Glue Data Quality to maintain data quality of S3 Tables and Apache Iceberg tables on general purpose S3 buckets. We'll discuss strategies for verifying the quality of published data and how these integrated technologies can be used to implement effective data quality workflows.]]></description>
		
		
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		<item>
		<title>Build an analytics pipeline that is resilient to Avro schema changes using Amazon Athena</title>
		<link>https://noise.getoto.net/2025/07/25/build-an-analytics-pipeline-that-is-resilient-to-avro-schema-changes-using-amazon-athena/</link>
		
		<dc:creator><![CDATA[Mohammad Sabeel]]></dc:creator>
		<pubDate>Fri, 25 Jul 2025 16:33:06 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=60f1626244e9a4ca8d6fab8d53784395</guid>

					<description><![CDATA[This post demonstrates how to build a solution by combining Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue Data Catalog for schema management, and Amazon Athena for one-time querying. We'll focus specifically on handling Avro-formatted data in partitioned S3 buckets, where schemas can change frequently while providing consistent query capabilities across all data regardless of schema versions.]]></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>Scale your AWS Glue for Apache Spark jobs with R type, G.12X, and G.16X workers</title>
		<link>https://noise.getoto.net/2025/07/18/scale-your-aws-glue-for-apache-spark-jobs-with-r-type-g-12x-and-g-16x-workers/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 17 Jul 2025 21:07:52 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e4fa979937bb3357a0c1b11581b4ebec</guid>

					<description><![CDATA[This post demonstrates how AWS Glue R type, G.12X, and G.16X workers help you scale up your AWS Glue for Apache Spark jobs.]]></description>
		
		
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		<item>
		<title>Introducing Jobs in Amazon SageMaker</title>
		<link>https://noise.getoto.net/2025/07/15/introducing-jobs-in-amazon-sagemaker/</link>
		
		<dc:creator><![CDATA[Chiho Sugimoto]]></dc:creator>
		<pubDate>Tue, 15 Jul 2025 19:10:51 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[This post demonstrates how the new jobs experience works in SageMaker Unified Studio.]]></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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=bf160435528c9c3f548ec4de6c09fb0d</guid>

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

			</item>
		<item>
		<title>Geospatial data lakes with Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/07/11/geospatial-data-lakes-with-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Jeremy Spell]]></dc:creator>
		<pubDate>Thu, 10 Jul 2025 21:52:05 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7528f9ab5d0c2ac8286628348193974c</guid>

					<description><![CDATA[In this post, we review how to set up Redshift Serverless to use geospatial data contained within a data lake to enhance maps in ArcGIS Pro. This technique helps builders and GIS analysts use available datasets in data lakes and transform it in Amazon Redshift to further enrich the data before presenting it on a map.]]></description>
		
		
		<enclosure url="" length="0" type="" />

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