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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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 exploration.

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Social and Environmental FactorsData-intensive systems are a technological building block supporting Big Data and Data Science applications. Rapid emergence of these systems is driving the development of new books and courses to provide education in the techniques and technologies needed to extract knowledge from large datasets.

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Preface,Data-intensive systems are a technological building block supporting Big Data and Data Science applications. Rapid emergence of these systems is driving the development of new books and courses to provide education in the techniques and technologies needed to extract knowledge from large datasets.

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MapReduce Algorithms and Patterns,In this chapter, I will show you a few examples of the most common types of MapReduce patterns and algorithms. They will guide your thinking on how to encode typical operations in a MapReduce way. This should guide you in a way you think about your own coding challenges.

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Introduction,and 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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