Big Data
Reference documents in Big Data.
Documents in Big Data
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This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations.
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This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.
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This open access book is a comprehensive exploration into the pivotal role of data in shaping our contemporary society. The variety of topics and examples provided on the general topic are a highlight.
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Big data is all about vast amounts of data, i.e., large datasets measured in terabytes or petabytes or even more. Big data helps numerous companies to generate valuable insights about the products or the services they offer. In recent years, almost every company uses big data technology to refine its marketing campaigns and techniques.
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The sublinear computation paradigm is proposed here in order to support innovation in the big data era. Focus on sublinear algorithms, sublinear data structures, and sublinear modelling.
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This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.
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With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Apache Spark from R to tackle big data and big compute problems.
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This book offers an introduction to the Apache Spark ecosystem, you will learn a wide array of concepts about PySpark in Data Mining, Text Mining, Machine Learning and Deep Learning.
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This book teaches you how to write parallel programs for multicore machines, compute clusters, GPU accelerators, and big data map-reduce jobs, in the Java language, with the free, easy-to-use, object-oriented Parallel Java 2 Library. The book also covers how to measure the performance of parallel programs and how to design the programs to run as fast as possible.
