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	<title>Tom Romano &#8211; Noise</title>
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		<title>Optimize Amazon S3 Tables queries with Amazon Redshift</title>
		<link>https://noise.getoto.net/2026/05/14/optimize-amazon-s3-tables-queries-with-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Tom Romano]]></dc:creator>
		<pubDate>Thu, 14 May 2026 16:58:31 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
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					<description><![CDATA[This is the third post in our S3 Tables and Amazon Redshift series. The first post covered getting started with querying Apache Iceberg tables, and the second post walked through enterprise-scale governance and access controls. In this post, you address those performance and usability gaps with three different approaches.]]></description>
		
		
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		<title>Empower your Jira data in a data lake with Amazon AppFlow and AWS Glue</title>
		<link>https://noise.getoto.net/2023/08/01/empower-your-jira-data-in-a-data-lake-with-amazon-appflow-and-aws-glue/</link>
		
		<dc:creator><![CDATA[Tom Romano]]></dc:creator>
		<pubDate>Tue, 01 Aug 2023 16:14:01 +0000</pubDate>
				<category><![CDATA[Amazon AppFlow]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]]]></description>
		
		
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