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	<title>benchmark &#8211; Noise</title>
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		<title>Run Apache Spark workloads 3.5 times faster with Amazon EMR 6.9</title>
		<link>https://noise.getoto.net/2023/01/30/run-apache-spark-workloads-3-5-times-faster-with-amazon-emr-6-9/</link>
		
		<dc:creator><![CDATA[Sekar Srinivasan]]></dc:creator>
		<pubDate>Mon, 30 Jan 2023 18:34:02 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[benchmark]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Performance]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we analyze the results from our benchmark tests running a TPC-DS application on open-source Apache Spark and then on Amazon EMR 6.9, which comes with an optimized Spark runtime that is compatible with open-source Spark. We walk through a detailed cost analysis and finally provide step-by-step instructions to run the benchmark. With Amazon EMR 6.9.0, you can now run your Apache Spark 3.x applications faster and at lower cost without requiring any changes to your applications. In our performance benchmark tests, derived from TPC-DS performance tests at 3 TB scale, we found the EMR runtime for Apache Spark 3.3.0 provides a 3.5 times (using total runtime) performance improvement on average over open-source Apache Spark 3.3.0.]]></description>
		
		
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