Distributed Computing in Big Data Analytics: Concepts, Technologies and Applications (Scalable Computing and Communications)

ISBN 10: 331986713X / ISBN 13: 9783319867137

Verlag: Springer, 2018

Beschreibung

New.

Bestandsnummer des Verkäufers ERICA797331986713X6

Bibliografische Details

• Titel: Distributed Computing in Big Data Analytics: Concepts, Technologies and Applications (Scalable Computing and Communications)
• Verlag: Springer
• Veröffentlichungsdatum: 2018
• Sprache: Englisch
• ISBN-10: 331986713X
• ISBN-13: 9783319867137
• Bindung: Paperback
• Zustand: New
• Buchtyp: book

Über diesen Titel

Inhaltsangabe

Addresses key concepts and patterns of distributed computing to provide practitioners with insight while designing big data analytics use cases

Details how different big data technologies leverage those key concepts and patterns of distributed computing

Includes applications, such as IoT, cognitive analytics, social media analytics and scientific data analytics

Reseña del editor

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.