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	<title>EMR &#8211; Noise</title>
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		<title>Detect and resolve HBase inconsistencies faster with AI on Amazon EMR</title>
		<link>https://noise.getoto.net/2026/05/12/detect-and-resolve-hbase-inconsistencies-faster-with-ai-on-amazon-emr/</link>
		
		<dc:creator><![CDATA[Yu-Ting Su]]></dc:creator>
		<pubDate>Tue, 12 May 2026 15:56:41 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon OpenSearch]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache HBase]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[EMR]]></category>
		<category><![CDATA[Experience-Based Acceleration]]></category>
		<category><![CDATA[Kiro]]></category>
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					<description><![CDATA[In this post, we show you how to build an AI-powered troubleshooting solution using Amazon OpenSearch Service vector search and intelligent analysis. This solution reduces HBase inconsistency resolution from hours to minutes and root cause identification from days to hours through natural language queries over operational data. This democratizes HBase troubleshooting capabilities across teams and reducing dependency on specialized expertise.]]></description>
		
		
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		<title>A guide to Airflow worker pool optimization in Amazon MWAA</title>
		<link>https://noise.getoto.net/2026/05/01/a-guide-to-airflow-worker-pool-optimization-in-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Boyko Radulov]]></dc:creator>
		<pubDate>Fri, 01 May 2026 15:41:26 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[EMR]]></category>
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					<description><![CDATA[Optimizing the Airflow worker pool configuration in Amazon Managed Workflows for Apache Airflow (Amazon MWAA), the AWS fully managed Apache Airflow service, is an important yet often overlooked strategy for scaling workflow operations. Tasks queued for longer periods can create the illusion that additional workers are the solution, when in reality the root cause might […]]]></description>
		
		
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		<title>Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2023/03/03/build-incremental-data-pipelines-to-load-transactional-data-changes-using-aws-dms-delta-2-0-and-amazon-emr-serverless/</link>
		
		<dc:creator><![CDATA[Sankar Sundaram]]></dc:creator>
		<pubDate>Fri, 03 Mar 2023 18:54:50 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Delta]]></category>
		<category><![CDATA[EMR]]></category>
		<category><![CDATA[EMR Serverless]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. A solution to this problem is to use AWS Database Migration Service (AWS DMS) for migrating historical and real-time transactional data into the data lake. You can then […]]]></description>
		
		
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		<item>
		<title>A new Spark plugin for CPU and memory profiling</title>
		<link>https://noise.getoto.net/2022/05/13/a-new-spark-plugin-for-cpu-and-memory-profiling/</link>
		
		<dc:creator><![CDATA[Bo Xiong]]></dc:creator>
		<pubDate>Fri, 13 May 2022 19:56:42 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[Amazon CodeGuru]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[CodeGuru]]></category>
		<category><![CDATA[EMR]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Industries]]></category>
		<category><![CDATA[profiling]]></category>
		<category><![CDATA[Spark]]></category>
		<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[Introduction Have you ever wondered if there are low-hanging optimization opportunities to improve the performance of a Spark app? Profiling can help you gain visibility regarding the runtime characteristics of the Spark app to identify its bottlenecks and inefficiencies. We’re excited to announce the release of a new Spark plugin that enables profiling for JVM […]]]></description>
		
		
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