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	<title>Dhiraj Thakur &#8211; Noise</title>
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		<title>Build a real-time GDPR-aligned Apache Iceberg data lake</title>
		<link>https://noise.getoto.net/2023/02/24/build-a-real-time-gdpr-aligned-apache-iceberg-data-lake/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Fri, 24 Feb 2023 18:29:28 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
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					<description><![CDATA[Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. But regulations such as the General Data Protection Regulation (GDPR) have created obligations for data operators who must be able to erase or […]]]></description>
		
		
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		<title>Announcing the AWS Well-Architected Data Analytics Lens</title>
		<link>https://noise.getoto.net/2022/11/17/announcing-the-latest-version-of-the-aws-well-architected-data-analytics-lens/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Thu, 17 Nov 2022 19:28:02 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[We are delighted to announce the latest version of the Data Analytics Lens, an AWS Well-Architected whitepaper. AWS Well-Architected provides a consistent approach to evaluate architectures and implement scalable designs. The AWS Well-Architected Framework is based on six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. With the framework, cloud architects, system architects, engineers, and developers can build secure, high-performance, resilient, and efficient infrastructure for their applications and workloads.]]></description>
		
		
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		<title>Create single output files for recipe jobs using AWS Glue DataBrew</title>
		<link>https://noise.getoto.net/2022/09/07/create-single-output-files-for-recipe-jobs-using-aws-glue-databrew/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Wed, 07 Sep 2022 17:38:09 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
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					<description><![CDATA[AWS Glue DataBrew offers over 350 pre-built transformations to automate data preparation tasks (such as filtering anomalies, standardizing formats, and correcting invalid values) that would otherwise require days or weeks writing hand-coded transformations. You can now choose single or multiple output files instead of autogenerated files for your DataBrew recipe jobs. You can generate a […]]]></description>
		
		
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		<title>Write prepared data directly into JDBC-supported destinations using AWS Glue DataBrew</title>
		<link>https://noise.getoto.net/2022/04/18/write-prepared-data-directly-into-jdbc-supported-destinations-using-aws-glue-databrew/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Mon, 18 Apr 2022 15:48:54 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
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					<description><![CDATA[AWS Glue DataBrew offers over 250 pre-built transformations to automate data preparation tasks (such as filtering anomalies, standardizing formats, and correcting invalid values) that would otherwise require days or weeks writing hand-coded transformations. You can now write cleaned and normalized data directly into JDBC-supported databases and data warehouses without having to move large amounts of […]]]></description>
		
		
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		<title>Automate code reviews with Amazon CodeGuru Reviewer</title>
		<link>https://noise.getoto.net/2022/02/15/automate-code-reviews-with-amazon-codeguru-reviewer/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Tue, 15 Feb 2022 16:34:32 +0000</pubDate>
				<category><![CDATA[Amazon CodeGuru]]></category>
		<category><![CDATA[AWS CodeCommit]]></category>
		<category><![CDATA[devops]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[A common problem in software development is accidentally or unintentionally merging code with bugs, defects, or security vulnerabilities into your main branch. Finding and mitigating these faulty lines of code deployed to the production environment can cause severe outages in running applications and can cost unnecessary time and effort to fix. Amazon CodeGuru Reviewer tackles […]]]></description>
		
		
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		<title>Query an Apache Hudi dataset in an Amazon S3 data lake with Amazon Athena part 1: Read-optimized queries</title>
		<link>https://noise.getoto.net/2021/07/16/query-an-apache-hudi-dataset-in-an-amazon-s3-data-lake-with-amazon-athena-part-1-read-optimized-queries/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Fri, 16 Jul 2021 20:42:34 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
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					<description><![CDATA[On July 16, 2021, Amazon Athena upgraded its Apache Hudi integration with new features and support for Hudi’s latest 0.8.0 release. Hudi is an open-source storage management framework that provides incremental data processing primitives for Hadoop-compatible data lakes. This upgraded integration adds the latest community improvements to Hudi along with important new features including snapshot […]]]></description>
		
		
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		<title>Data preparation using an Amazon RDS for MySQL database with AWS Glue DataBrew</title>
		<link>https://noise.getoto.net/2021/07/14/data-preparation-using-an-amazon-rds-for-mysql-database-with-aws-glue-databrew/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Wed, 14 Jul 2021 15:17:47 +0000</pubDate>
				<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
		<category><![CDATA[RDS for MySQL]]></category>
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					<description><![CDATA[With AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, or Amazon Aurora and Amazon Relational Database Service (Amazon RDS) databases. You can choose from over 250 built-in […]]]></description>
		
		
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		<title>Data preparation using Amazon Redshift with AWS Glue DataBrew</title>
		<link>https://noise.getoto.net/2021/07/01/data-preparation-using-amazon-redshift-with-aws-glue-databrew/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Thu, 01 Jul 2021 20:55:06 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
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					<description><![CDATA[With AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, Amazon Aurora, and other Amazon Relational Database Service (Amazon RDS) databases. You can choose from over 250 built-in functions to merge, pivot, and transpose […]]]></description>
		
		
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		<item>
		<title>Build a data lake using Amazon Kinesis Data Streams for Amazon DynamoDB and Apache Hudi</title>
		<link>https://noise.getoto.net/2021/03/04/build-a-data-lake-using-amazon-kinesis-data-streams-for-amazon-dynamodb-and-apache-hudi/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Thu, 04 Mar 2021 21:15:05 +0000</pubDate>
				<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
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					<description><![CDATA[Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and online order transaction data to develop customer order fulfillment applications, improve customer satisfaction, and get insights into sales revenue to create a promotional offer for the customer. It’s essential to store these data points in a centralized data […]]]></description>
		
		
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		<item>
		<title>Building AWS Data Lake visualizations with Amazon Athena and Tableau</title>
		<link>https://noise.getoto.net/2021/02/02/building-aws-data-lake-visualizations-with-amazon-athena-and-tableau/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Tue, 02 Feb 2021 20:54:43 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[AWS Big Data]]></category>
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					<description><![CDATA[Amazon Athena is an interactive query service that makes it easy to analyze data in a data lake using standard SQL. One of the key elements of Athena is that you only pay for the queries you run. This is an attractive feature because there is no hardware to set up, manage, or maintain. You […]]]></description>
		
		
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