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	<title>Aarthi Srinivasan &#8211; Noise</title>
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		<title>Create AWS Glue Data Catalog views using cross-account definer roles</title>
		<link>https://noise.getoto.net/2026/01/09/create-aws-glue-data-catalog-views-using-cross-account-definer-roles/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 08 Jan 2026 22:45:19 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we demonstrate how to use cross-account IAM definer roles with AWS Glue Data Catalog views. We show how data owner accounts can create and manage views in a central governance account while maintaining security and control over their data assets.]]></description>
		
		
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		<item>
		<title>Create and update Apache Iceberg tables with partitions in the AWS Glue Data Catalog using the AWS SDK and AWS CloudFormation</title>
		<link>https://noise.getoto.net/2025/12/18/create-and-update-apache-iceberg-tables-with-partitions-in-the-aws-glue-data-catalog-using-the-aws-sdk-and-aws-cloudformation/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 18 Dec 2025 21:22:43 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we show how to create and update Iceberg tables with partitions in the Data Catalog using the AWS SDK and AWS CloudFormation.]]></description>
		
		
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			</item>
		<item>
		<title>Use trusted identity propagation for Apache Spark interactive sessions in Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2025/10/31/use-trusted-identity-propagation-for-apache-spark-interactive-sessions-in-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Fri, 31 Oct 2025 20:55:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we provide step-by-step instructions to set up Amazon EMR on EC2, EMR Serverless, and AWS Glue within SageMaker Unified Studio, enabled with trusted identity propagation. We use the setup to illustrate how different IAM Identity Center users can run their Spark sessions, using each compute setup, within the same project in SageMaker Unified Studio. We show how each user will see only tables or part of tables that they’re granted access to in Lake Formation.]]></description>
		
		
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			</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>
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					<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>
		<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>
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					<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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		<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>
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					<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>AWS Lake Formation 2023 year in review</title>
		<link>https://noise.getoto.net/2024/01/18/aws-lake-formation-2023-year-in-review/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 18 Jan 2024 19:11:35 +0000</pubDate>
				<category><![CDATA[AWS Lake Formation]]></category>
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					<description><![CDATA[AWS Lake Formation and the AWS Glue Data Catalog form an integral part of a data governance solution for data lakes built on Amazon Simple Storage Service (Amazon S3) with multiple AWS analytics services integrating with them. In 2022, we talked about the enhancements we had done to these services. We continue to listen to […]]]></description>
		
		
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			</item>
		<item>
		<title>Introducing hybrid access mode for AWS Glue Data Catalog to secure access using AWS Lake Formation and IAM and Amazon S3 policies</title>
		<link>https://noise.getoto.net/2023/09/27/introducing-hybrid-access-mode-for-aws-glue-data-catalog-to-secure-access-using-aws-lake-formation-and-iam-and-amazon-s3-policies/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Tue, 26 Sep 2023 23:24:10 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
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					<description><![CDATA[To ease the transition of data lake permissions from an IAM and S3 model to Lake Formation, we’re introducing a hybrid access mode for AWS Glue Data Catalog. This feature lets you secure and access the cataloged data using both Lake Formation permissions and IAM and S3 permissions. Hybrid access mode allows data administrators to onboard Lake Formation permissions selectively and incrementally, focusing on one data lake use case at a time. For example, say you have an existing extract, transform and load (ETL) data pipeline that uses the IAM and S3 policies to manage data access. Now you want to allow your data analysts to explore or query the same data using Amazon Athena. You can grant access to the data analysts using Lake Formation permissions, to include fine-grained controls as needed, without changing access for your ETL data pipelines.]]></description>
		
		
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			</item>
		<item>
		<title>Configure cross-Region table access with the AWS Glue Catalog and AWS Lake Formation</title>
		<link>https://noise.getoto.net/2023/08/03/configure-cross-region-table-access-with-the-aws-glue-catalog-and-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 03 Aug 2023 14:55:55 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
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					<description><![CDATA[Today’s modern data lakes span multiple accounts, AWS Regions, and lines of business in organizations. Companies also have employees and do business across multiple geographic regions and even around the world. It’s important that their data solution gives them the ability to share and access data securely and safely across Regions. The AWS Glue Data […]]]></description>
		
		
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			</item>
		<item>
		<title>Query your Apache Hive metastore with AWS Lake Formation permissions</title>
		<link>https://noise.getoto.net/2023/07/20/query-your-apache-hive-metastore-with-aws-lake-formation-permissions/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Thu, 20 Jul 2023 17:48:06 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. There are two key components to Apache Hive: the Hive SQL query engine and the Hive metastore (HMS). The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, […]]]></description>
		
		
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			</item>
		<item>
		<title>Enable cross-account sharing with direct IAM principals using AWS Lake Formation Tags</title>
		<link>https://noise.getoto.net/2023/01/18/enable-cross-account-sharing-with-direct-iam-principals-using-aws-lake-formation-tags/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Tue, 17 Jan 2023 23:07:13 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[With AWS Lake Formation, you can build data lakes with multiple AWS accounts in a variety of ways. For example, you could build a data mesh, implementing a centralized data governance model and decoupling data producers from the central governance. Such data lakes enable the data as an asset paradigm and unleash new possibilities with […]]]></description>
		
		
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