Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	August 2018
Genre:	EDV / Informatik
	Application software / B / Communications engineering / telecommunications / Communications Engineering, Networks / Computer Communication Networks / Computer communication systems / computer science / Database Management / database programming / Databases / Electrical Engineering / Information Retrieval / Information Systems Applications (incl. Internet) / Information Systems Applications (incl.Internet) / Internet searching
ISBN:	9783319867137
EAN-Code:	9783319867137
Verlag:	Springer Nature EN
Einband:	Kartoniert
Sprache:	English
Serie:	Scalable Computing and Communications
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	273 gr
Seiten:	162
Illustration:	X, 162 p. 72 illus., 63 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	Previously published in hardcover
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.