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	<title>Sakti Mishra &#8211; Noise</title>
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		<title>Top analytics announcements of AWS re:Invent 2024</title>
		<link>https://noise.getoto.net/2025/02/26/top-analytics-announcements-of-aws-reinvent-2024/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 26 Feb 2025 19:36:44 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
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					<description><![CDATA[AWS re:Invent 2024, the flagship annual conference, took place December 2–6, 2024, in Las Vegas, bringing together thousands of cloud enthusiasts, innovators, and industry leaders from around the globe. Analytics remained one of the key focus areas this year, with significant updates and innovations aimed at helping businesses harness their data more efficiently and accelerate insights. In this post, we walk you through the top analytics announcements from re:Invent 2024 and explore how these innovations can help you unlock the full potential of your data.]]></description>
		
		
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		<title>Enforce fine-grained access control on data lake tables using AWS Glue 5.0 integrated with AWS Lake Formation</title>
		<link>https://noise.getoto.net/2024/12/04/enforce-fine-grained-access-control-on-data-lake-tables-using-aws-glue-5-0-integrated-with-aws-lake-formation/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 19:06:42 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
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					<description><![CDATA[AWS Glue 5.0 supports fine-grained access control (FGAC) based on your policies defined in AWS Lake Formation. FGAC enables you to granularly control access to your data lake resources at the table, column, and row levels. This post demonstrates how to enforce FGAC on AWS Glue 5.0 through Lake Formation permissions.]]></description>
		
		
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		<title>Unstructured data management and governance using AWS AI/ML and analytics services</title>
		<link>https://noise.getoto.net/2023/10/25/unstructured-data-management-and-governance-using-aws-ai-ml-and-analytics-services/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 25 Oct 2023 18:52:03 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Comprehend]]></category>
		<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Rekognition]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Amazon Textract]]></category>
		<category><![CDATA[Amazon Transcribe]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<category><![CDATA[Thought Leadership]]></category>
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					<description><![CDATA[In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.]]></description>
		
		
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		<title>Implement a CDC-based UPSERT in a data lake using Apache Iceberg and AWS Glue</title>
		<link>https://noise.getoto.net/2022/06/15/implement-a-cdc-based-upsert-in-a-data-lake-using-apache-iceberg-and-aws-glue/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 15 Jun 2022 20:18:49 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[As the implementation of data lakes and modern data architecture increases, customers’ expectations around its features also increase, which include ACID transaction, UPSERT, time travel, schema evolution, auto compaction, and many more. By default, Amazon Simple Storage Service (Amazon S3) objects are immutable, which means you can’t update records in your data lake because it […]]]></description>
		
		
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		<title>Orchestrating an AWS Glue DataBrew job and Amazon Athena query with AWS Step Functions</title>
		<link>https://noise.getoto.net/2021/01/06/orchestrating-an-aws-glue-databrew-job-and-amazon-athena-query-with-aws-step-functions/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Wed, 06 Jan 2021 20:05:06 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[As the industry grows with more data volume, big data analytics is becoming a common requirement in data analytics and machine learning (ML) use cases. Also, as we start building complex data engineering or data analytics pipelines, we look for a simpler orchestration mechanism with graphical user interface-based ETL (extract, transform, load) tools. Recently, AWS [&#8230;]]]></description>
		
		
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