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	<title>Pathik Shah &#8211; Noise</title>
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		<title>Transform your data to Amazon S3 Tables with Amazon Athena</title>
		<link>https://noise.getoto.net/2025/08/15/transform-your-data-to-amazon-s3-tables-with-amazon-athena/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Fri, 15 Aug 2025 20:25:21 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[This post demonstrates how Amazon Athena CREATE TABLE AS SELECT (CTAS) simplifies the data transformation process through a practical example: migrating an existing Parquet dataset into Amazon S3 Tables.]]></description>
		
		
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		<title>Query AWS Glue Data Catalog views using Amazon Athena and Amazon Redshift</title>
		<link>https://noise.getoto.net/2024/08/08/query-aws-glue-data-catalog-views-using-amazon-athena-and-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Thu, 08 Aug 2024 16:52:38 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
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					<description><![CDATA[Glue Data Catalog views is a new feature of the AWS Glue Data Catalog that customers can use to create a common view schema and single metadata container that can hold view-definitions in different dialects that can be used across engines such as Amazon Redshift and Amazon Athena. In this blog post, we will show how you can define and query a Data Catalog view on top of open source table formats such as Iceberg across Athena and Amazon Redshift. We will also show you the configurations needed to restrict access to the underlying database and tables. To follow along, we have provided an AWS CloudFormation template.]]></description>
		
		
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		<title>Use Amazon Athena with Spark SQL for your open-source transactional table formats</title>
		<link>https://noise.getoto.net/2024/01/24/use-amazon-athena-with-spark-sql-for-your-open-source-transactional-table-formats/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Wed, 24 Jan 2024 20:06:00 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[serverless]]></category>
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					<description><![CDATA[In this post, we show you how to use Spark SQL in Amazon Athena notebooks and work with Iceberg, Hudi, and Delta Lake table formats. We demonstrate common operations such as creating databases and tables, inserting data into the tables, querying data, and looking at snapshots of the tables in Amazon S3 using Spark SQL in Athena.]]></description>
		
		
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		<title>Run Spark SQL on Amazon Athena Spark</title>
		<link>https://noise.getoto.net/2023/10/24/run-spark-sql-on-amazon-athena-spark/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Mon, 23 Oct 2023 21:37:30 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[At AWS re:Invent 2022, Amazon Athena launched support for Apache Spark. With this launch, Amazon Athena supports two open-source query engines: Apache Spark and Trino. Athena Spark allows you to build Apache Spark applications using a simplified notebook experience on the Athena console or through Athena APIs. Athena Spark notebooks support PySpark and notebook magics […]]]></description>
		
		
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		<title>Explore your data lake using Amazon Athena for Apache Spark</title>
		<link>https://noise.getoto.net/2022/12/01/explore-your-data-lake-using-amazon-athena-for-apache-spark/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Thu, 01 Dec 2022 17:25:21 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
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					<description><![CDATA[Amazon Athena now enables data analysts and data engineers to enjoy the easy-to-use, interactive, serverless experience of Athena with Apache Spark in addition to SQL. You can now use the expressive power of Python and build interactive Apache Spark applications using a simplified notebook experience on the Athena console or through Athena APIs. For interactive […]]]></description>
		
		
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		<title>Analyze Amazon Ion datasets using Amazon Athena</title>
		<link>https://noise.getoto.net/2022/05/19/analyze-amazon-ion-datasets-using-amazon-athena/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Wed, 18 May 2022 22:47:31 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
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					<description><![CDATA[Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Amazon Ion is a richly typed, self-describing, hierarchical data serialization format […]]]></description>
		
		
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		<title>Run queries 3x faster with up to 70% cost savings on the latest Amazon Athena engine</title>
		<link>https://noise.getoto.net/2021/12/02/run-queries-3x-faster-with-up-to-70-cost-savings-on-the-latest-amazon-athena-engine/</link>
		
		<dc:creator><![CDATA[Pathik Shah]]></dc:creator>
		<pubDate>Thu, 02 Dec 2021 01:59:32 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
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					<description><![CDATA[Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In November 2020, Athena announced the General Availability of the V2 […]]]></description>
		
		
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