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	<title>Luis Morales &#8211; Noise</title>
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		<title>Implement Apache Flink near-online data enrichment patterns</title>
		<link>https://noise.getoto.net/2023/11/15/implement-apache-flink-near-online-data-enrichment-patterns/</link>
		
		<dc:creator><![CDATA[Luis Morales]]></dc:creator>
		<pubDate>Wed, 15 Nov 2023 17:32:21 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Thought Leadership]]></category>
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					<description><![CDATA[You can use several approaches to enrich your real-time data in Amazon Managed Service for Apache Flink depending on your use case and Apache Flink abstraction level. Each method has different effects on the throughput, network traffic, and CPU (or memory) utilization. For a general overview of data enrichment patterns, refer to Common streaming data enrichment patterns in Amazon Managed Service for Apache Flink. This post covers how you can implement data enrichment for near-online streaming events with Apache Flink and how you can optimize performance. To compare the performance of the enrichment patterns, we ran performance testing based on synthetic data. The result of this test is useful as a general reference. It’s important to note that the actual performance for your Flink workload will depend on various and different factors, such as API latency, throughput, size of the event, and cache hit ratio.]]></description>
		
		
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		<title>Perform Amazon Kinesis load testing with Locust</title>
		<link>https://noise.getoto.net/2023/08/10/perform-amazon-kinesis-load-testing-with-locust/</link>
		
		<dc:creator><![CDATA[Luis Morales]]></dc:creator>
		<pubDate>Thu, 10 Aug 2023 14:50:22 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Firehose]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Building a streaming data solution requires thorough testing at the scale it will operate in a production environment. Streaming applications operating at scale often handle large volumes of up to GBs per second, and it’s challenging for developers to simulate high-traffic Amazon Kinesis-based applications to generate such load easily. Amazon Kinesis Data Streams and Amazon […]]]></description>
		
		
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