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www.altexsoft.com | ||
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aneesh.mataroa.blog
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| | | | | [AI summary] The article discusses the evolution of big data processing technologies from supercomputing to Hadoop MapReduce and finally to Apache Spark, emphasizing the importance of understanding the 'why' behind tools and how they address scalability and efficiency challenges. | |
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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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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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gist.github.com
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| | | Camunda 7 search "half-court shot" initial results - half-court-shot-initial-results.md | ||
