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	<title>Amazon SageMaker Lakehouse &#8211; Noise</title>
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	<link>https://noise.getoto.net</link>
	<description>The collective thoughts of the interwebz</description>
	<lastBuildDate>Tue, 18 Nov 2025 23:01:03 +0000</lastBuildDate>
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		<title>Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog</title>
		<link>https://noise.getoto.net/2025/11/19/cross-account-lakehouse-governance-with-amazon-s3-tables-and-sagemaker-catalog/</link>
		
		<dc:creator><![CDATA[Sneha Rao]]></dc:creator>
		<pubDate>Tue, 18 Nov 2025 23:01:03 +0000</pubDate>
				<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.]]></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>
		<item>
		<title>The Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control for federated catalogs</title>
		<link>https://noise.getoto.net/2025/08/29/the-amazon-sagemaker-lakehouse-architecture-now-supports-tag-based-access-control-for-federated-catalogs/</link>
		
		<dc:creator><![CDATA[Sandeep Adwankar]]></dc:creator>
		<pubDate>Fri, 29 Aug 2025 18:31:04 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e2f83e9b935dccee1f4c8f9ac3c4cc11</guid>

					<description><![CDATA[We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless.]]></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>
		<item>
		<title>Guide to adopting Amazon SageMaker Unified Studio from ATPCO’s Journey</title>
		<link>https://noise.getoto.net/2025/08/18/guide-to-adopting-amazon-sagemaker-unified-studio-from-atpcos-journey/</link>
		
		<dc:creator><![CDATA[Mitesh Patel]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 19:03:39 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[data governance]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9c4b746d91d0b72f02696bc03d3a15d3</guid>

					<description><![CDATA[ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio.]]></description>
		
		
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			</item>
		<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>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a8202149a8b2da33920869c2277d1a5a</guid>

					<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>
		<item>
		<title>Streamline the path from data to insights with new Amazon SageMaker Catalog capabilities</title>
		<link>https://noise.getoto.net/2025/07/16/streamline-the-path-from-data-to-insights-with-new-amazon-sagemaker-catalog-capabilities/</link>
		
		<dc:creator><![CDATA[Donnie Prakoso]]></dc:creator>
		<pubDate>Tue, 15 Jul 2025 23:49:24 +0000</pubDate>
				<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Summit New York]]></category>
		<category><![CDATA[Featured]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=20c7e39bfdd3ccc464288b46be0c5869</guid>

					<description><![CDATA[Amazon SageMaker has introduced three new capabilities—Amazon QuickSight integration for dashboard creation, governance, and sharing, Amazon S3 Unstructured Data Integration for cataloging documents and media files, and automatic data onboarding from Lakehouse—that eliminate data silos by unifying structured and unstructured data management, visualization, and governance in a single experience.]]></description>
		
		
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			</item>
		<item>
		<title>Develop and monitor a Spark application using existing data in Amazon S3 with Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2025/07/09/develop-and-monitor-a-spark-application-using-existing-data-in-amazon-s3-with-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Amit Maindola]]></dc:creator>
		<pubDate>Wed, 09 Jul 2025 19:31:24 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=cf4b208134e6c7fb239b6312a43259f8</guid>

					<description><![CDATA[In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools.]]></description>
		
		
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			</item>
		<item>
		<title>Reduce time to access your transactional data for analytical processing using the power of Amazon SageMaker Lakehouse and zero-ETL</title>
		<link>https://noise.getoto.net/2025/06/16/reduce-time-to-access-your-transactional-data-for-analytical-processing-using-the-power-of-amazon-sagemaker-lakehouse-and-zero-etl/</link>
		
		<dc:creator><![CDATA[Avijit Goswami]]></dc:creator>
		<pubDate>Mon, 16 Jun 2025 19:25:32 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=70dd6b8062406ef394b1f94cc1c5958b</guid>

					<description><![CDATA[In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). With this integration, you can now seamlessly onboard the changed data from OLTP systems to a unified lakehouse and expose the same to analytical applications for consumptions using Apache Iceberg APIs from new SageMaker Unified Studio.]]></description>
		
		
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			</item>
		<item>
		<title>Simplify real-time analytics with zero-ETL from Amazon DynamoDB to Amazon SageMaker Lakehouse</title>
		<link>https://noise.getoto.net/2025/06/06/simplify-real-time-analytics-with-zero-etl-from-amazon-dynamodb-to-amazon-sagemaker-lakehouse/</link>
		
		<dc:creator><![CDATA[Narayani Ambashta]]></dc:creator>
		<pubDate>Fri, 06 Jun 2025 16:46:57 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Solutions Implementations]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2fa2d7c02e89fe3cacffcb6e91148f8a</guid>

					<description><![CDATA[At AWS re:Invent 2024, we introduced a no code zero-ETL integration between Amazon DynamoDB and Amazon SageMaker Lakehouse, simplifying how organizations handle data analytics and AI workflows. In this post, we share how to set up this zero-ETL integration from DynamoDB to your SageMaker Lakehouse environment.]]></description>
		
		
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		<item>
		<title>Unify streaming and analytical data with Amazon Data Firehose and Amazon SageMaker Lakehouse</title>
		<link>https://noise.getoto.net/2025/05/27/unify-streaming-and-analytical-data-with-amazon-data-firehose-and-amazon-sagemaker-lakehouse/</link>
		
		<dc:creator><![CDATA[Kalyan Janaki]]></dc:creator>
		<pubDate>Tue, 27 May 2025 16:44:08 +0000</pubDate>
				<category><![CDATA[Amazon Data Firehose]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=58c7c5e64daa017e908e69028e00b703</guid>

					<description><![CDATA[In this post, we show you how to create Iceberg tables in Amazon SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.]]></description>
		
		
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		<item>
		<title>Access Amazon Redshift Managed Storage tables through Apache Spark on AWS Glue and Amazon EMR using Amazon SageMaker Lakehouse</title>
		<link>https://noise.getoto.net/2025/05/15/access-amazon-redshift-managed-storage-tables-through-apache-spark-on-aws-glue-and-amazon-emr-using-amazon-sagemaker-lakehouse/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 15 May 2025 17:25:07 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ab399f94a2d744b0393181394f2c82a5</guid>

					<description><![CDATA[With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This post describes how to integrate data on RMS tables through Apache Spark using SageMaker Unified Studio, Amazon EMR 7.5.0 and higher, and AWS Glue 5.0.]]></description>
		
		
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		<item>
		<title>Enhance governance with asset type usage policies in Amazon SageMaker</title>
		<link>https://noise.getoto.net/2025/05/12/enhance-governance-with-asset-type-usage-policies-in-amazon-sagemaker/</link>
		
		<dc:creator><![CDATA[Pradeep Misra]]></dc:creator>
		<pubDate>Mon, 12 May 2025 20:21:20 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f6916b04108c30c333a68177b67afbca</guid>

					<description><![CDATA[In this post, we introduce authorization policies for custom asset types—a new governance capability in Amazon SageMaker that gives organizations fine-grained control over who can create and manage assets using specific templates. This feature enhances data governance by allowing teams to enforce usage policies that align with business and security requirements across the organization.]]></description>
		
		
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		<item>
		<title>Configure cross-account access of Amazon SageMaker Lakehouse multi-catalog tables using AWS Glue 5.0 Spark</title>
		<link>https://noise.getoto.net/2025/05/09/configure-cross-account-access-of-amazon-sagemaker-lakehouse-multi-catalog-tables-using-aws-glue-5-0-spark/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Fri, 09 May 2025 17:18:44 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></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=cc26012180b2a67d5ac39577ed6929eb</guid>

					<description><![CDATA[In this post, we show you how to share an Amazon Redshift table and Amazon S3 based Iceberg table from the account that owns the data to another account that consumes the data. In the recipient account, we run a join query on the shared data lake and data warehouse tables using Spark in AWS Glue 5.0. We walk you through the complete cross-account setup and provide the Spark configuration in a Python notebook.]]></description>
		
		
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		<item>
		<title>Access your existing data and resources through Amazon SageMaker Unified Studio, Part 1: AWS Glue Data Catalog and Amazon Redshift</title>
		<link>https://noise.getoto.net/2025/04/28/access-your-existing-data-and-resources-through-amazon-sagemaker-unified-studio-part-1-aws-glue-data-catalog-and-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Lakshmi Nair]]></dc:creator>
		<pubDate>Mon, 28 Apr 2025 16:35:23 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=92e3bff33f76adbdb3c671e3b0b2bcc7</guid>

					<description><![CDATA[This series of posts demonstrates how you can onboard and access existing AWS data sources using SageMaker Unified Studio. This post focuses on onboarding existing AWS Glue Data Catalog tables and database tables available in Amazon Redshift.]]></description>
		
		
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		<item>
		<title>Access your existing data and resources through Amazon SageMaker Unified Studio, Part 2: Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR</title>
		<link>https://noise.getoto.net/2025/04/28/access-your-existing-data-and-resources-through-amazon-sagemaker-unified-studio-part-2-amazon-s3-amazon-rds-amazon-dynamodb-and-amazon-emr/</link>
		
		<dc:creator><![CDATA[Lakshmi Nair]]></dc:creator>
		<pubDate>Mon, 28 Apr 2025 16:35:03 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f5b14287407ebc15d95dfc71e7acf40f</guid>

					<description><![CDATA[In this post we discuss integrating additional vital data sources such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon EMR clusters. We demonstrate how to configure the necessary permissions, establish connections, and effectively use these resources within SageMaker Unified Studio. Whether you’re working with object storage, relational databases, NoSQL databases, or big data processing, this post can help you seamlessly incorporate your existing data infrastructure into your SageMaker Unified Studio workflows.]]></description>
		
		
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		<item>
		<title>AWS Weekly Roundup: Amazon Q Developer, AWS Account Management updates, and more (April 28, 2025)</title>
		<link>https://noise.getoto.net/2025/04/28/aws-weekly-roundup-amazon-q-developer-aws-account-management-updates-and-more-april-28-2025/</link>
		
		<dc:creator><![CDATA[Matheus Guimaraes]]></dc:creator>
		<pubDate>Mon, 28 Apr 2025 16:00:25 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Cognito]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<category><![CDATA[Week in Review]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2c9d8d78f9e51f2497094ca7317d01a0</guid>

					<description><![CDATA[Summit season is in full throttle! If you haven’t been to an AWS Summit, I highly recommend you check one out that’s nearby. They are large-scale all-day events where you can attend talks, watch interesting demos and activities, connect with AWS and industry people, and more. Best of all, they are free—so all you need […]]]></description>
		
		
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			</item>
		<item>
		<title>Amazon SageMaker Lakehouse now supports attribute-based access control</title>
		<link>https://noise.getoto.net/2025/04/24/amazon-sagemaker-lakehouse-now-supports-attribute-based-access-control/</link>
		
		<dc:creator><![CDATA[Sandeep Adwankar]]></dc:creator>
		<pubDate>Thu, 24 Apr 2025 20:16:32 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Identity and Access Management (IAM)]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=fdcb8abb606391792379f8dd4550f5ec</guid>

					<description><![CDATA[Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerate your analytics with Amazon S3 Tables and Amazon SageMaker Lakehouse</title>
		<link>https://noise.getoto.net/2025/04/17/accelerate-your-analytics-with-amazon-s3-tables-and-amazon-sagemaker-lakehouse/</link>
		
		<dc:creator><![CDATA[Sandeep Adwankar]]></dc:creator>
		<pubDate>Thu, 17 Apr 2025 20:31:07 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Identity and Access Management (IAM)]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=3f509fa8a8e4dc0a5661cc6515d4798c</guid>

					<description><![CDATA[Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.]]></description>
		
		
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