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finnstats.com | ||
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www.madewithtea.com
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| | | | | This article is about aggregates in stateful stream processing in general. I write about the differences between Apache Spark and Apache Kafka Streams along concrete code examples. Further, I list the requirements which we might like to see covered by a stream processing framework. | |
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www.onehouse.ai
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| | | | | The Lambda architecture has been widely used to handle both batch and real-time data streams. However, as organizations seek more efficient and scalable solutions to keep up with exploding data volumes, they find the limitations of Lambda architectures. Check out this summary of a recent talk on Apache Beam from David Regalado, Engineering VP at a stealth-mode startup and Google Cloud Champion Innovator. | |
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timilearning.com
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| | | | | In the first lecture of this series, I wrote about MapReduce as a distributed computation framework. MapReduce partitions the input data across worker nodes, which process data in two stages: map and reduce. While MapReduce was innovative, it was inefficient for iterative and more complex computations. Researchers at UC Berkeley invented Spark to deal with these limitations. | |
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claroty.com
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| | | Protect your critical infrastructure with industrial cybersecurity solutions. Claroty offers advanced technology and expertise to safeguard your industrial systems from cyber threats. Learn more about industrial cybersecurity and secure your operations today. | ||
