Distributed Computing in Big Data Analytics

Big data technologies are used on high volume of data of various types to achieve any type of analytics in a fast and predictable way 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 literatures available in big data analytics world do not in a holistic way which can highlight the relation between big data analytics and distributed processing for ease of understanding and use of the practitioners.

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.

• ISBN: 978-3-319-59833-8
• Editorial: Springer
• Encuadernacion: Cartoné
• Fecha Publicación: 06/10/2017
• Nº Volúmenes: 1
• Idioma: Inglés