Titlebook: Data-intensive Systems; Principles and Funda Tomasz Wiktorski Book 2019 The Author(s), under exclusive license to Springer Nature Switzerla

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Tomasz WiktorskiIntroduces students to core concepts before they continue with advanced technical literature.Only strictly educational text on the market not tied to just one software framework.Clear didactic approac

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https://doi.org/10.1007/978-3-030-04603-3Data-intensive systems; Big Data; Data Science; MapReduce; Hadoop

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978-3-030-04602-6The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

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The Management of Outline Slidesand grow very fast. I also explain hardware trends that drive a need for new paradigms for data processing, which lead to new data processing systems—Data-Intensive Systems. These systems are an essential building block in Data Science application.

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https://doi.org/10.1007/978-1-349-07069-5e hour. I have made a small sample of a dataset available so that you can efficiently run the examples without a bigger cluster. I want to show that you can use all of the basic technologies almost immediately. Presented examples are purposefully very simple to provide you with a basic working skele

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Ratio, Proportion and Averages,nical solutions that enable the use of map and reduce functions in practice for parallel processing of big data. First of all, a distributed file system, like Hadoop Distributed File System (HDFS), which ensures delivery of unique subsets of the whole dataset to each map instance.

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https://doi.org/10.1007/978-1-349-07069-5rimarily focus on the two main components: Hadoop Distributed File System (HDFS) and MapReduce (MR). These two components provide the basic Hadoop functionality that most other elements rely on. I will also shortly cover other components, mostly to provide you with a basis for further independent ex

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