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	<title>S3 Select &#8211; Noise</title>
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		<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>
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					<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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