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	<title>AWS Glue Data Quality &#8211; Noise</title>
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		<title>Accelerate your data quality journey for lakehouse architecture with Amazon SageMaker, Apache Iceberg on AWS, Amazon S3 tables, and AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2025/07/28/accelerate-your-data-quality-journey-for-lakehouse-architecture-with-amazon-sagemaker-apache-iceberg-on-aws-amazon-s3-tables-and-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Brody Pearman]]></dc:creator>
		<pubDate>Mon, 28 Jul 2025 18:09:37 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
		<category><![CDATA[data governance]]></category>
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					<description><![CDATA[This post explores how you can use AWS Glue Data Quality to maintain data quality of S3 Tables and Apache Iceberg tables on general purpose S3 buckets. We'll discuss strategies for verifying the quality of published data and how these integrated technologies can be used to implement effective data quality workflows.]]></description>
		
		
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		<title>Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2024/12/10/build-write-audit-publish-pattern-with-apache-iceberg-branching-and-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Tomohiro Tanaka]]></dc:creator>
		<pubDate>Mon, 09 Dec 2024 22:24:12 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
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					<description><![CDATA[This post explores robust strategies for maintaining data quality when ingesting data into Apache Iceberg tables using AWS Glue Data Quality and Iceberg branches. We discuss two common strategies to verify the quality of published data. We dive deep into the Write-Audit-Publish (WAP) pattern, demonstrating how it works with Apache Iceberg.]]></description>
		
		
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		<title>Implement data quality checks on Amazon Redshift data assets and integrate with Amazon DataZone</title>
		<link>https://noise.getoto.net/2024/08/15/implement-data-quality-checks-on-amazon-redshift-data-assets-and-integrate-with-amazon-datazone/</link>
		
		<dc:creator><![CDATA[Lakshmi Nair]]></dc:creator>
		<pubDate>Thu, 15 Aug 2024 16:16:22 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
		<category><![CDATA[data governance]]></category>
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					<description><![CDATA[In this post, we show how to capture the data quality metrics for data assets produced in Amazon Redshift. With Amazon DataZone, the data owner can directly import the technical metadata of a Redshift database table and views to the Amazon DataZone project’s inventory. As these data assets gets imported into Amazon DataZone, it bypasses the AWS Glue Data Catalog, creating a gap in data quality integration. This post proposes a solution to enrich the Amazon Redshift data asset with data quality scores and KPI metrics.]]></description>
		
		
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		<title>Getting started with AWS Glue Data Quality for ETL Pipelines</title>
		<link>https://noise.getoto.net/2022/12/16/getting-started-with-aws-glue-data-quality-for-etl-pipelines/</link>
		
		<dc:creator><![CDATA[Deenbandhu Prasad]]></dc:creator>
		<pubDate>Fri, 16 Dec 2022 18:04:15 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
		<category><![CDATA[ETL Pipelines]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Today, hundreds of thousands of customers use data lakes for analytics and machine learning. However, data engineers have to cleanse and prepare this data before it can be used. The underlying data has to be accurate and recent for customer to make confident business decisions. Otherwise, data consumers lose trust in the data and make […]]]></description>
		
		
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