Non-Fiction Books:

Distributed Computing in Big Data Analytics

Concepts, Technologies and Applications

Format

Hardback

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Distributed Computing in Big Data Analytics
$318.00
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Description

Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.
Release date NZ
September 8th, 2017
Contributors
Edited by Ganesh Chandra Deka Edited by Robin Singh Bhadoria Edited by Sourav Mazumder
Country of Publication
Switzerland
Edition
1st ed. 2017
Illustrations
63 Illustrations, color; 9 Illustrations, black and white; X, 162 p. 72 illus., 63 illus. in color.
Imprint
Springer International Publishing AG
Pages
162
ISBN-13
9783319598338
Product ID
26805365

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