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	<title>AWS Lake Formation &#8211; Noise</title>
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		<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>Implement fine-grained access control for Iceberg tables using Amazon EMR on EKS integrated with AWS Lake Formation</title>
		<link>https://noise.getoto.net/2025/10/24/implement-fine-grained-access-control-for-iceberg-tables-using-amazon-emr-on-eks-integrated-with-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Tejal Patel]]></dc:creator>
		<pubDate>Fri, 24 Oct 2025 20:39:25 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=476b706e38da34c4c074e7eaafd1b212</guid>

					<description><![CDATA[On February 6th 2025, AWS introduced fine-grained access control based on AWS Lake Formation for EMR on EKS from Amazon EMR 7.7 and higher version. You can now significantly enhance your data governance and security frameworks using this feature. In this post, we demonstrate how to implement FGAC on Apache Iceberg tables using EMR on EKS with 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>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>
		<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="" />

			</item>
		<item>
		<title>How Stifel built a modern data platform using AWS Glue and an event-driven domain architecture</title>
		<link>https://noise.getoto.net/2025/07/07/how-stifel-built-a-modern-data-platform-using-aws-glue-and-an-event-driven-domain-architecture/</link>
		
		<dc:creator><![CDATA[Amit Maindola]]></dc:creator>
		<pubDate>Mon, 07 Jul 2025 14:22:19 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Experience-Based Acceleration]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<category><![CDATA[Thought Leadership]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=d7d1155705679766c1b43ca02808c1dc</guid>

					<description><![CDATA[In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Enforce table level access control on data lake tables using AWS Glue 5.0 with AWS Lake Formation</title>
		<link>https://noise.getoto.net/2025/06/30/enforce-table-level-access-control-on-data-lake-tables-using-aws-glue-5-0-with-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Layth Yassin]]></dc:creator>
		<pubDate>Mon, 30 Jun 2025 16:34:00 +0000</pubDate>
				<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=2804a8fb1846f3c640c0c6f5483cad3f</guid>

					<description><![CDATA[In this post, we show you how to enforce FTA control on AWS Glue 5.0 through Lake Formation permissions.]]></description>
		
		
		<enclosure url="" length="0" type="" />

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

			</item>
		<item>
		<title>Using AWS Glue Data Catalog views with Apache Spark in EMR Serverless and Glue 5.0</title>
		<link>https://noise.getoto.net/2025/06/05/using-aws-glue-data-catalog-views-with-apache-spark-in-emr-serverless-and-glue-5-0/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 05 Jun 2025 16:45:46 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></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=4dc3225699218a5cc7c935d60886a438</guid>

					<description><![CDATA[In this post, we guide you through the process of creating a Data Catalog view using EMR Serverless, adding the SQL dialect to the view for Athena, sharing it with another account using LF-Tags, and then querying the view in the recipient account using a separate EMR Serverless workspace and AWS Glue 5.0 Spark job and Athena. This demonstration showcases the versatility and cross-account capabilities of Data Catalog views and access through various AWS analytics services.]]></description>
		
		
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			</item>
		<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>
		<item>
		<title>Automate replication of row-level security from AWS Lake Formation to Amazon QuickSight</title>
		<link>https://noise.getoto.net/2025/05/07/automate-replication-of-row-level-security-from-aws-lake-formation-to-amazon-quicksight/</link>
		
		<dc:creator><![CDATA[Vetri Natarajan]]></dc:creator>
		<pubDate>Wed, 07 May 2025 15:23:20 +0000</pubDate>
				<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4ec73fecd8595ef62d8c5f7b19384e90</guid>

					<description><![CDATA[This post outlines a solution to automatically replicate the entitlements for readers from the source (AWS Lake Formation) to Amazon QuickSight. This solution can be used even when the authentication method in Amazon QuickSight is not using IAM Identity Center and can work with both direct query and SPICE datasets in Amazon QuickSight.]]></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>
		<title>Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode</title>
		<link>https://noise.getoto.net/2025/04/21/read-and-write-apache-iceberg-tables-using-aws-lake-formation-hybrid-access-mode/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Mon, 21 Apr 2025 17:10:48 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ea72e3b07988c055c2768739b153e1a3</guid>

					<description><![CDATA[In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables.]]></description>
		
		
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		<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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		<item>
		<title>Hybrid big data analytics with Amazon EMR on AWS Outposts</title>
		<link>https://noise.getoto.net/2025/01/29/hybrid-big-data-analytics-with-amazon-emr-on-aws-outposts/</link>
		
		<dc:creator><![CDATA[Shoukat Ghouse]]></dc:creator>
		<pubDate>Wed, 29 Jan 2025 21:20:35 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Outposts rack]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=42fae58772180fd59cc176eec312865a</guid>

					<description><![CDATA[In this post, we dive into the transformative features of EMR on Outposts, showcasing its flexibility as a native hybrid data analytics service that allows seamless data access and processing both on premises and in the cloud.]]></description>
		
		
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		<title>Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction</title>
		<link>https://noise.getoto.net/2024/12/19/accelerate-queries-on-apache-iceberg-tables-through-aws-glue-auto-compaction/</link>
		
		<dc:creator><![CDATA[Navnit Shukla]]></dc:creator>
		<pubDate>Thu, 19 Dec 2024 15:05:38 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=575e2cfb7e5a6d39501662408016d3b9</guid>

					<description><![CDATA[In this post, we explore new features of the AWS Glue Data Catalog, which now supports improved automatic compaction of Iceberg tables for streaming data, making it straightforward for you to keep your transactional data lakes consistently performant. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance]]></description>
		
		
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		<title>Enforce fine-grained access control on data lake tables using AWS Glue 5.0 integrated with AWS Lake Formation</title>
		<link>https://noise.getoto.net/2024/12/04/enforce-fine-grained-access-control-on-data-lake-tables-using-aws-glue-5-0-integrated-with-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 19:06:42 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c4586bcad5925a4d1ce9f98754b062b4</guid>

					<description><![CDATA[AWS Glue 5.0 supports fine-grained access control (FGAC) based on your policies defined in AWS Lake Formation. FGAC enables you to granularly control access to your data lake resources at the table, column, and row levels. This post demonstrates how to enforce FGAC on AWS Glue 5.0 through Lake Formation permissions.]]></description>
		
		
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			</item>
		<item>
		<title>Read and write S3 Iceberg table using AWS Glue Iceberg Rest Catalog from Open Source Apache Spark</title>
		<link>https://noise.getoto.net/2024/12/04/read-and-write-s3-iceberg-table-using-aws-glue-iceberg-rest-catalog-from-open-source-apache-spark/</link>
		
		<dc:creator><![CDATA[Raj Ramasubbu]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 18:53:59 +0000</pubDate>
				<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c5bc8b139350ad7d1878b7fc1e855ee6</guid>

					<description><![CDATA[In this post, we will explore how to harness the power of Open source Apache Spark and configure a third-party engine to work with AWS Glue Iceberg REST Catalog. The post will include details on how to perform read/write data operations against Amazon S3 tables with AWS Lake Formation managing metadata and underlying data access using temporary credential vending.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes</title>
		<link>https://noise.getoto.net/2024/12/04/how-anz-institutional-division-built-a-federated-data-platform-to-enable-their-domain-teams-to-build-data-products-to-support-business-outcomes/</link>
		
		<dc:creator><![CDATA[Leo Ramsamy]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 18:32:11 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Thought Leadership]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1a77fb7087aae46f8d8a069d7f8722f3</guid>

					<description><![CDATA[ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.]]></description>
		
		
		<enclosure url="" length="0" type="" />

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