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	<title>Amazon Kinesis Data Streams &#8211; Noise</title>
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		<title>Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications</title>
		<link>https://noise.getoto.net/2024/05/28/architectural-patterns-for-real-time-analytics-using-amazon-kinesis-data-streams-part-2-ai-applications/</link>
		
		<dc:creator><![CDATA[Raghavarao Sodabathina]]></dc:creator>
		<pubDate>Tue, 28 May 2024 16:49:25 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis Data Streams]]></category>
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		<category><![CDATA[Generative BI]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! In this fast-paced world, Kinesis Data Streams stands out as a versatile and robust solution to tackle a wide range of use cases with real-time data, from dashboarding to powering artificial intelligence (AI) applications. In this series, we […]]]></description>
		
		
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		<title>LaunchDarkly’s journey from ingesting 1 TB to 100 TB per day with Amazon Kinesis Data Streams</title>
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		<dc:creator><![CDATA[Mike Zorn]]></dc:creator>
		<pubDate>Thu, 15 Dec 2022 17:52:07 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis Data Streams]]></category>
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		<category><![CDATA[AWS Big Data]]></category>
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		<category><![CDATA[Kinesis Data Streams]]></category>
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					<description><![CDATA[This post was co-written with Mike Zorn, Software Architect at LaunchDarkly as the lead author. LaunchDarkly’s feature management platform enables customers to release features and measure their impact. As part of this platform, SDKs gather event data, and the event ingestion platform consumes and analyzes this data to measure impact. As the platform launched and […]]]></description>
		
		
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		<title>How MEDHOST’s cardiac risk prediction successfully leveraged AWS analytic services</title>
		<link>https://noise.getoto.net/2021/08/23/how-medhosts-cardiac-risk-prediction-successfully-leveraged-aws-analytic-services/</link>
		
		<dc:creator><![CDATA[Pandian Velayutham]]></dc:creator>
		<pubDate>Mon, 23 Aug 2021 17:14:59 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Kinesis Data Firehose]]></category>
		<category><![CDATA[Amazon Kinesis Data Streams]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[Industries]]></category>
		<category><![CDATA[Kinesis Data Firehose]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
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					<description><![CDATA[MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]]]></description>
		
		
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		<title>Stream CDC into an Amazon S3 data lake in Parquet format with AWS DMS</title>
		<link>https://noise.getoto.net/2020/09/08/stream-cdc-into-an-amazon-s3-data-lake-in-parquet-format-with-aws-dms/</link>
		
		<dc:creator><![CDATA[Viral Shah]]></dc:creator>
		<pubDate>Tue, 08 Sep 2020 20:46:14 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Kinesis Data Streams]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Database Migration Service]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
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					<description><![CDATA[Most organizations generate data in real time and ever-increasing volumes. Data is captured from a variety of sources, such as transactional and reporting databases, application logs, customer-facing websites, and external feeds. Companies want to capture, transform, and analyze this time-sensitive data to improve customer experiences, increase efficiency, and drive innovations. With increased data volume and [&#8230;]]]></description>
		
		
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