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	<title>Nitin Aggarwal &#8211; Noise</title>
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		<title>Build a data quality score card using AWS Glue DataBrew, Amazon Athena, and Amazon QuickSight</title>
		<link>https://noise.getoto.net/2021/06/04/build-a-data-quality-score-card-using-aws-glue-databrew-amazon-athena-and-amazon-quicksight/</link>
		
		<dc:creator><![CDATA[Nitin Aggarwal]]></dc:creator>
		<pubDate>Thu, 03 Jun 2021 21:56:22 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[Data quality plays an important role while building an extract, transform, and load (ETL) pipeline for sending data to downstream analytical applications and machine learning (ML) models. The analogy “garbage in, garbage out” is apt at describing why it’s important to filter out bad data before further processing. Continuously monitoring data quality and comparing it […]]]></description>
		
		
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