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	<title>Vishal Pathak &#8211; Noise</title>
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		<title>Ingest streaming data to Apache Hudi tables using AWS Glue and Apache Hudi DeltaStreamer</title>
		<link>https://noise.getoto.net/2022/10/06/ingest-streaming-data-to-apache-hudi-tables-using-aws-glue-and-apache-hudi-deltastreamer/</link>
		
		<dc:creator><![CDATA[Vishal Pathak]]></dc:creator>
		<pubDate>Thu, 06 Oct 2022 17:06:19 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Expert (400)]]></category>
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					<description><![CDATA[In today’s world with technology modernization, the need for near-real-time streaming use cases has increased exponentially. Many customers are continuously consuming data from different sources, including databases, applications, IoT devices, and sensors. Organizations may need to ingest that streaming data into data lakes built on Amazon Simple Storage Service (Amazon S3). You may also need […]]]></description>
		
		
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		<title>Writing to Apache Hudi tables using AWS Glue Custom Connector</title>
		<link>https://noise.getoto.net/2021/01/20/writing-to-apache-hudi-tables-using-aws-glue-custom-connector/</link>
		
		<dc:creator><![CDATA[Vishal Pathak]]></dc:creator>
		<pubDate>Wed, 20 Jan 2021 20:12:16 +0000</pubDate>
				<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[In today’s world, most organizations have to tackle the 3 V’s of variety, volume and velocity of big data. In this blog post, we talk about dealing with the variety and volume aspects of big data. The challenge of dealing with the variety involves processing data from various SQL and NoSQL systems. This variety can […]]]></description>
		
		
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		<title>Creating a source to Lakehouse data replication pipe using Apache Hudi, AWS Glue, AWS DMS, and Amazon Redshift</title>
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		<dc:creator><![CDATA[Vishal Pathak]]></dc:creator>
		<pubDate>Tue, 17 Nov 2020 17:11:45 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Services (S3)]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[Most customers have their applications backed by various sql and nosql systems on prem and on cloud. Since the data is in various independent systems, customers struggle to derive meaningful info by combining data from all of these sources. Hence, customers create data lakes to bring their data in a single place. Typically, a replication [&#8230;]]]></description>
		
		
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		<title>Developing AWS Glue ETL jobs locally using a container</title>
		<link>https://noise.getoto.net/2020/09/08/developing-aws-glue-etl-jobs-locally-using-a-container/</link>
		
		<dc:creator><![CDATA[Vishal Pathak]]></dc:creator>
		<pubDate>Tue, 08 Sep 2020 16:23:42 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. In the fourth post of the series, we discussed optimizing memory management. In this post, we focus on writing ETL scripts for AWS Glue jobs locally. AWS Glue is built on [&#8230;]]]></description>
		
		
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