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	<title>Amazon Redshift Spectrum &#8211; Noise</title>
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		<title>Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics</title>
		<link>https://noise.getoto.net/2024/10/01/accelerate-amazon-redshift-data-lake-queries-with-aws-glue-data-catalog-column-statistics/</link>
		
		<dc:creator><![CDATA[Kalaiselvi Kamaraj]]></dc:creator>
		<pubDate>Tue, 01 Oct 2024 20:45:00 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Optimization]]></category>
		<category><![CDATA[Performance]]></category>
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					<description><![CDATA[Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. In this post, we highlight the performance improvements we observed using industry standard TPC-DS benchmarks. Overall execution time of TPC-DS 3 TB benchmark improved by 3x. Some of the queries in our benchmark experienced up to 12x speed up.]]></description>
		
		
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		<title>Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone</title>
		<link>https://noise.getoto.net/2024/08/15/seamless-integration-of-data-lake-and-data-warehouse-using-amazon-redshift-spectrum-and-amazon-datazone/</link>
		
		<dc:creator><![CDATA[Lakshmi Nair]]></dc:creator>
		<pubDate>Thu, 15 Aug 2024 16:18:49 +0000</pubDate>
				<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[data governance]]></category>
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					<description><![CDATA[Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate data lakes or warehouses—hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several […]]]></description>
		
		
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		<title>Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store</title>
		<link>https://noise.getoto.net/2023/10/26/create-train-and-deploy-amazon-redshift-ml-model-integrating-features-from-amazon-sagemaker-feature-store/</link>
		
		<dc:creator><![CDATA[Anirban Sinha]]></dc:creator>
		<pubDate>Thu, 26 Oct 2023 17:19:48 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Studio]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting […]]]></description>
		
		
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		<title>Automate data archival for Amazon Redshift time series tables</title>
		<link>https://noise.getoto.net/2022/10/04/automate-data-archival-for-amazon-redshift-time-series-tables/</link>
		
		<dc:creator><![CDATA[Nita Shah]]></dc:creator>
		<pubDate>Tue, 04 Oct 2022 17:40:59 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all of your data using standard SQL. Tens of thousands of customers today rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud […]]]></description>
		
		
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		<title>Accelerate self-service analytics with Amazon Redshift Query Editor V2</title>
		<link>https://noise.getoto.net/2021/11/08/accelerate-self-service-analytics-with-amazon-redshift-query-editor-v2/</link>
		
		<dc:creator><![CDATA[Bhanu Pittampally]]></dc:creator>
		<pubDate>Mon, 08 Nov 2021 17:28:52 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[query editor]]></category>
		<category><![CDATA[redshift]]></category>
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					<description><![CDATA[Amazon Redshift is a fast, fully managed cloud data warehouse. Tens of thousands of customers use Amazon Redshift as their analytics platform. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses. Amazon Redshift provides a web-based query editor in addition to supporting connectivity […]]]></description>
		
		
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