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	<title>Karthick Shanmugam, Srinivasa Are &#8211; Noise</title>
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		<title>Verisk cuts processing time and storage costs with Amazon Redshift and lakehouse</title>
		<link>https://noise.getoto.net/2026/02/16/verisk-cuts-processing-time-and-storage-costs-with-amazon-redshift-and-lakehouse/</link>
		
		<dc:creator><![CDATA[Karthick Shanmugam, Srinivasa Are]]></dc:creator>
		<pubDate>Mon, 16 Feb 2026 18:10:44 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Verisk, a catastrophe modeling SaaS provider serving insurance and reinsurance companies worldwide, cut processing time from hours to minutes-level aggregations while reducing storage costs by implementing a lakehouse architecture with Amazon Redshift and Apache Iceberg. If you’re managing billions of catastrophe modeling records across hurricanes, earthquakes, and wildfires, this approach eliminates the traditional compute-versus-cost trade-off by separating storage from processing power. In this post, we examine Verisk’s lakehouse implementation, focusing on four architectural decisions that delivered measurable improvements.]]></description>
		
		
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