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	<title>Mitesh Patel &#8211; Noise</title>
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		<title>Guide to adopting Amazon SageMaker Unified Studio from ATPCO’s Journey</title>
		<link>https://noise.getoto.net/2025/08/18/guide-to-adopting-amazon-sagemaker-unified-studio-from-atpcos-journey/</link>
		
		<dc:creator><![CDATA[Mitesh Patel]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 19:03:39 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[data governance]]></category>
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					<description><![CDATA[ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio.]]></description>
		
		
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		<title>Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue</title>
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		<dc:creator><![CDATA[Mitesh Patel]]></dc:creator>
		<pubDate>Mon, 31 Jul 2023 18:41:23 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Database Migration Service]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Data has become an integral part of most companies, and the complexity of data processing is increasing rapidly with the exponential growth in the amount and variety of data. Data engineering teams are faced with the following challenges: Manipulating data to make it consumable by business users Building and improving extract, transform, and load (ETL) […]]]></description>
		
		
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