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	<title>Kinesis Data Streams &#8211; Noise</title>
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	<lastBuildDate>Mon, 03 Nov 2025 22:00:31 +0000</lastBuildDate>
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		<title>Amazon Kinesis Data Streams launches On-demand Advantage for instant throughput increases and streaming at scale</title>
		<link>https://noise.getoto.net/2025/11/04/amazon-kinesis-data-streams-launches-on-demand-advantage-for-instant-throughput-increases-and-streaming-at-scale/</link>
		
		<dc:creator><![CDATA[Pratik Patel]]></dc:creator>
		<pubDate>Mon, 03 Nov 2025 22:00:31 +0000</pubDate>
				<category><![CDATA[announcements]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=27101372d8a595b045edb7c6f06b24ec</guid>

					<description><![CDATA[Today, AWS announced the new Amazon Kinesis Data Streams On-demand Advantage mode, which includes warm throughput capability and an updated pricing structure. With this feature you can enable instant scaling for traffic surges while optimizing costs for consistent streaming workloads. In this post, we explore this new feature, including key use cases, configuration options, pricing considerations, and best practices for optimal performance.]]></description>
		
		
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			</item>
		<item>
		<title>Amazon Kinesis Data Streams now supports 10x larger record sizes: Simplifying real-time data processing</title>
		<link>https://noise.getoto.net/2025/10/28/amazon-kinesis-data-streams-now-supports-10x-larger-record-sizes-simplifying-real-time-data-processing/</link>
		
		<dc:creator><![CDATA[Sumant Nemmani]]></dc:creator>
		<pubDate>Tue, 28 Oct 2025 19:23:30 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9c5cbc2af01719f3122b3aedeca265ba</guid>

					<description><![CDATA[Today, AWS announced that Amazon Kinesis Data Streams now supports record sizes up to 10MiB – a tenfold increase from the previous limit. In this post, we explore Amazon Kinesis Data Streams large record support, including key use cases, configuration of maximum record sizes, throttling considerations, and best practices for optimal performance.]]></description>
		
		
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			</item>
		<item>
		<title>Build a streaming data mesh using Amazon Kinesis Data Streams</title>
		<link>https://noise.getoto.net/2025/09/10/build-a-streaming-data-mesh-using-amazon-kinesis-data-streams/</link>
		
		<dc:creator><![CDATA[Felix John]]></dc:creator>
		<pubDate>Wed, 10 Sep 2025 16:50:11 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=0c8017a3cfb68dae5d61188f8b2d4153</guid>

					<description><![CDATA[AWS provides two primary solutions for streaming ingestion and storage: Amazon Managed Streaming for Apache Kafka (Amazon MSK) or Amazon Kinesis Data Streams. These services are key to building a streaming mesh on AWS. In this post, we explore how to build a streaming mesh using Kinesis Data Streams.]]></description>
		
		
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			</item>
		<item>
		<title>How Airties achieved scalability and cost-efficiency by moving from Kafka to Amazon Kinesis Data Streams</title>
		<link>https://noise.getoto.net/2025/05/29/how-airties-achieved-scalability-and-cost-efficiency-by-moving-from-kafka-to-amazon-kinesis-data-streams/</link>
		
		<dc:creator><![CDATA[Steven Aerts, Reza Radmehr]]></dc:creator>
		<pubDate>Thu, 29 May 2025 15:04:14 +0000</pubDate>
				<category><![CDATA[Amazon Data Firehose]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Experience-Based Acceleration]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c06a4f1bf025b654063e981735e80ad9</guid>

					<description><![CDATA[Airties is a wireless networking company that provides AI-driven solutions for enhancing home connectivity. This post explores the strategies the Airties team employed during their migration from Apache Kafka to Amazon Kinesis Data Streams, the challenges they overcame, and how they achieved a more efficient, scalable, and maintenance-free streaming infrastructure.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Deploy real-time analytics with StarTree for managed Apache Pinot on AWS</title>
		<link>https://noise.getoto.net/2025/03/13/deploy-real-time-analytics-with-startree-for-managed-apache-pinot-on-aws/</link>
		
		<dc:creator><![CDATA[Raj Ramasubbu]]></dc:creator>
		<pubDate>Thu, 13 Mar 2025 20:02:13 +0000</pubDate>
				<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Enterprise BI]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[Partner solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a30834ee730407b0a6294cda4240d956</guid>

					<description><![CDATA[In this post, we introduce StarTree as a managed solution on AWS for teams seeking the advantages of Pinot. We highlight the key distinctions between open-source Pinot and StarTree, and provide valuable insights for organizations considering a more streamlined approach to their real-time analytics infrastructure.]]></description>
		
		
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			</item>
		<item>
		<title>Governing streaming data in Amazon DataZone with the Data Solutions Framework on AWS</title>
		<link>https://noise.getoto.net/2025/02/27/governing-streaming-data-in-amazon-datazone-with-the-data-solutions-framework-on-aws/</link>
		
		<dc:creator><![CDATA[Vincent Gromakowski]]></dc:creator>
		<pubDate>Thu, 27 Feb 2025 17:16:51 +0000</pubDate>
				<category><![CDATA[Amazon DataZone]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2f1de91fbdb0914daaa3dbfc552ce601</guid>

					<description><![CDATA[In this post, we explore how AWS customers can extend Amazon DataZone to support streaming data such as Amazon Managed Streaming for Apache Kafka (Amazon MSK) topics. Developers and DevOps managers can use Amazon MSK, a popular streaming data service, to run Kafka applications and Kafka Connect connectors on AWS without becoming experts in operating it.]]></description>
		
		
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		<item>
		<title>Introducing the new Amazon Kinesis source connector for Apache Flink</title>
		<link>https://noise.getoto.net/2024/12/18/introducing-the-new-amazon-kinesis-source-connector-for-apache-flink/</link>
		
		<dc:creator><![CDATA[Lorenzo Nicora]]></dc:creator>
		<pubDate>Wed, 18 Dec 2024 15:02:31 +0000</pubDate>
				<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=03b028dd4d5bf7e87c751729a83f083c</guid>

					<description><![CDATA[On November 11, 2024, the Apache Flink community released a new version of AWS services connectors, an AWS open source contribution. This new release, version 5.0.0, introduces a new source connector to read data from Amazon Kinesis Data Streams. In this post, we explain how the new features of this connector can improve performance and reliability of your Apache Flink application.]]></description>
		
		
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			</item>
		<item>
		<title>Top 6 game changers from AWS that redefine streaming data</title>
		<link>https://noise.getoto.net/2024/12/17/top-6-game-changers-from-aws-that-redefine-streaming-data/</link>
		
		<dc:creator><![CDATA[Sai Maddali]]></dc:creator>
		<pubDate>Tue, 17 Dec 2024 16:35:19 +0000</pubDate>
				<category><![CDATA[Amazon Data Firehose]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=8d9d28cf8c04606ef5d53f5bba0beaa5</guid>

					<description><![CDATA[Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.]]></description>
		
		
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			</item>
		<item>
		<title>Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion</title>
		<link>https://noise.getoto.net/2024/11/11/use-amazon-kinesis-data-streams-to-deliver-real-time-data-to-amazon-opensearch-service-domains-with-amazon-opensearch-ingestion/</link>
		
		<dc:creator><![CDATA[M Mehrtens]]></dc:creator>
		<pubDate>Mon, 11 Nov 2024 21:11:49 +0000</pubDate>
				<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e363550bbd592b5fc24cca0a34784eeb</guid>

					<description><![CDATA[In this post, we show how to use Amazon Kinesis Data Streams to buffer and aggregate real-time streaming data for delivery into Amazon OpenSearch Service domains and collections using Amazon OpenSearch Ingestion. You can use this approach for a variety of use cases, from real-time log analytics to integrating application messaging data for real-time search. In this post, we focus on the use case for centralizing log aggregation for an organization that has a compliance need to archive and retain its log data.]]></description>
		
		
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		<item>
		<title>Reduce your compute costs for stream processing applications with Kinesis Client Library 3.0</title>
		<link>https://noise.getoto.net/2024/11/06/reduce-your-compute-costs-for-stream-processing-applications-with-kinesis-client-library-3-0/</link>
		
		<dc:creator><![CDATA[Minu Hong]]></dc:creator>
		<pubDate>Wed, 06 Nov 2024 20:19:13 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=aa7a676f867ca64c8e6fe7281e487287</guid>

					<description><![CDATA[We are excited to launch Kinesis Client Library 3.0, which enables you to reduce your stream processing cost by up to 33% compared to previous KCL versions. KCL 3.0 achieves this with a new load balancing algorithm that continuously monitors the resource utilization of workers and redistributes the load evenly to all workers. In this post, we discuss load balancing challenges in stream processing using a sample workload, demonstrating how uneven load distribution across workers increases processing costs.]]></description>
		
		
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			</item>
		<item>
		<title>Build a dynamic rules engine with Amazon Managed Service for Apache Flink</title>
		<link>https://noise.getoto.net/2024/10/03/build-a-dynamic-rules-engine-with-amazon-managed-service-for-apache-flink/</link>
		
		<dc:creator><![CDATA[Steven Carpenter]]></dc:creator>
		<pubDate>Thu, 03 Oct 2024 17:20:41 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis Analytics]]></category>
		<category><![CDATA[Amazon Kinesis Streams]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Industries]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Internet of Things]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[open source]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=970b69cf800e79a9ad4a2be0d65e30a2</guid>

					<description><![CDATA[This post demonstrates how to implement a dynamic rules engine using Amazon Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.]]></description>
		
		
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		<item>
		<title>Efficiently processing batched data using parallelization in AWS Lambda</title>
		<link>https://noise.getoto.net/2024/08/28/efficiently-processing-batched-data-using-parallelization-in-aws-lambda/</link>
		
		<dc:creator><![CDATA[Chris Munns]]></dc:creator>
		<pubDate>Wed, 28 Aug 2024 16:14:46 +0000</pubDate>
				<category><![CDATA[Amazon Simple Notification Service (SNS)]]></category>
		<category><![CDATA[Amazon Simple Queue Service (SQS)]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[serverless]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ebbc33e6eeb796b442024899730a74d4</guid>

					<description><![CDATA[This post is written by Anton Aleksandrov, Principal Solutions Architect, AWS Serverless Efficient message processing is crucial when handling large data volumes. By employing batching, distribution, and parallelization techniques, you can optimize the utilization of resources allocated to your AWS Lambda function. This post will demonstrate how to implement parallel data processing within the Lambda function handler, maximizing […]]]></description>
		
		
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		<item>
		<title>Build a real-time analytics solution with Apache Pinot on AWS</title>
		<link>https://noise.getoto.net/2024/08/06/build-a-real-time-analytics-solution-with-apache-pinot-on-aws/</link>
		
		<dc:creator><![CDATA[Raj Ramasubbu]]></dc:creator>
		<pubDate>Tue, 06 Aug 2024 14:40:13 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Embedded Analytics]]></category>
		<category><![CDATA[Enterprise BI]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Kinesis Data Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[open source]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=68aa62d40ec09633fed249018756fa39</guid>

					<description><![CDATA[In this, we will provide a step-by-step guide showing you how you can build a real-time OLAP datastore on Amazon Web Services (AWS) using Apache Pinot on Amazon Elastic Compute Cloud (Amazon EC2) and do near real-time visualization using Tableau. You can use Apache Pinot for batch processing use cases as well but, in this post, we will focus on a near real-time analytics use case.]]></description>
		
		
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		<item>
		<title>Serverless ICYMI Q2 2024</title>
		<link>https://noise.getoto.net/2024/07/02/serverless-icymi-q2-2024/</link>
		
		<dc:creator><![CDATA[Julian Wood]]></dc:creator>
		<pubDate>Tue, 02 Jul 2024 15:57:10 +0000</pubDate>
				<category><![CDATA[Amazon API Gateway]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon Elastic Container Service]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon GuardDuty]]></category>
		<category><![CDATA[Amazon Q]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Amazon Timestream]]></category>
		<category><![CDATA[AWS Amplify]]></category>
		<category><![CDATA[AWS AppSync]]></category>
		<category><![CDATA[AWS Fargate]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[serverless]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=5b42a625bba2a8a1aaca152fc285cd28</guid>

					<description><![CDATA[Welcome to the 26th edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. Every quarter, we share all the most recent product launches, feature enhancements, blog posts, webinars, live streams, and other interesting things that you might have missed! In case you missed our last ICYMI, check out what happened last […]]]></description>
		
		
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		<item>
		<title>Build a real-time streaming generative AI application using Amazon Bedrock, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams</title>
		<link>https://noise.getoto.net/2024/06/27/build-a-real-time-streaming-generative-ai-application-using-amazon-bedrock-amazon-managed-service-for-apache-flink-and-amazon-kinesis-data-streams/</link>
		
		<dc:creator><![CDATA[Felix John]]></dc:creator>
		<pubDate>Thu, 27 Jun 2024 18:10:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1159a156abce0e20ddc014d2448298a5</guid>

					<description><![CDATA[Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and Amazon Kinesis Data Streams for processing streaming data and Amazon Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.]]></description>
		
		
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		<item>
		<title>Uncover social media insights in real time using Amazon Managed Service for Apache Flink and Amazon Bedrock</title>
		<link>https://noise.getoto.net/2024/06/25/uncover-social-media-insights-in-real-time-using-amazon-managed-service-for-apache-flink-and-amazon-bedrock/</link>
		
		<dc:creator><![CDATA[Francisco Morillo]]></dc:creator>
		<pubDate>Tue, 25 Jun 2024 16:09:28 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=42c23deea11629898cc4fa5b025a6717</guid>

					<description><![CDATA[This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.]]></description>
		
		
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		<item>
		<title>Optimize write throughput for Amazon Kinesis Data Streams</title>
		<link>https://noise.getoto.net/2024/06/03/optimize-write-throughput-for-amazon-kinesis-data-streams/</link>
		
		<dc:creator><![CDATA[Buddhike de Silva]]></dc:creator>
		<pubDate>Mon, 03 Jun 2024 17:50:36 +0000</pubDate>
				<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=960af2fae74c9d57351bbfd52ae08a69</guid>

					<description><![CDATA[Amazon Kinesis Data Streams is used by many customers to capture, process, and store data streams at any scale. This level of unparalleled scale is enabled by dividing each data stream into multiple shards. Each shard in a stream has a 1 Mbps or 1,000 records per second write throughput limit. Whether your data streaming […]]]></description>
		
		
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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>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Business Intelligence]]></category>
		<category><![CDATA[Generative BI]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1511201c421ae930723c228bfbd4070a</guid>

					<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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		<item>
		<title>Build Spark Structured Streaming applications with the open source connector for Amazon Kinesis Data Streams</title>
		<link>https://noise.getoto.net/2024/05/24/build-spark-structured-streaming-applications-with-the-open-source-connector-for-amazon-kinesis-data-streams/</link>
		
		<dc:creator><![CDATA[Idan Maizlits]]></dc:creator>
		<pubDate>Fri, 24 May 2024 18:30:10 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9ee9370f3253e9de863569de240c9993</guid>

					<description><![CDATA[Apache Spark is a powerful big data engine used for large-scale data analytics. Its in-memory computing makes it great for iterative algorithms and interactive queries. You can use Apache Spark to process streaming data from a variety of streaming sources, including Amazon Kinesis Data Streams for use cases like clickstream analysis, fraud detection, and more. Kinesis Data Streams is a serverless streaming data service that makes it straightforward to capture, process, and store data streams at any scale. With the new open source Amazon Kinesis Data Streams Connector for Spark Structured Streaming, you can use the newer Spark Data Sources API. It also supports enhanced fan-out for dedicated read throughput and faster stream processing. In this post, we deep dive into the internal details of the connector and show you how to use it to consume and produce records from and to Kinesis Data Streams using Amazon EMR.]]></description>
		
		
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		<title>Krones real-time production line monitoring with Amazon Managed Service for Apache Flink</title>
		<link>https://noise.getoto.net/2024/03/28/krones-real-time-production-line-monitoring-with-amazon-managed-service-for-apache-flink/</link>
		
		<dc:creator><![CDATA[Florian Mair]]></dc:creator>
		<pubDate>Thu, 28 Mar 2024 16:21:06 +0000</pubDate>
				<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Kinesis Streams]]></category>
		<category><![CDATA[Amazon Managed Service for Apache Flink]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[apache flink]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=86a511dca918ce2c0bfe5e8d895ef20b</guid>

					<description><![CDATA[Krones provides breweries, beverage bottlers, and food producers all over the world with individual machines and complete production lines. This post shows how Krones built a streaming solution to monitor their lines, based on Amazon Kinesis and Amazon Managed Service for Apache Flink. These fully managed services reduce the complexity of building streaming applications with Apache Flink. Managed Service for Apache Flink manages the underlying Apache Flink components that provide durable application state, metrics, logs, and more, and Kinesis enables you to cost-effectively process streaming data at any scale.]]></description>
		
		
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