Big Data: Principles And Best Practices Of Scalable Realtime Data Systems

Nathan Marz Et Al

ISBN 10: 9351198065 / ISBN 13: 9789351198062

Published by Wiley India, 2015

About this Item

ISBN:9789351198062,Territorial restriction maybe printed on the book. This is an Int'l edition, ISBN and cover may differ from US edition, Contents same as US edition.

Seller Inventory # 2439589

Bibliographic Details

• Title: Big Data: Principles And Best Practices Of Scalable Realtime Data Systems
• Publisher: Wiley India
• Publication Date: 2015
• Language: English
• ISBN 10: 9351198065
• ISBN 13: 9789351198062
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling big data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you just need to take a different approach. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

Big Data teaches you to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built.

Big Data shows you how to build the back-end for a real-time service called SuperWebAnalytics.com—our version of Google Analytics. As you read, you'll discover that many standard RDBMS practices become unwieldy with large-scale data. To handle the complexities of Big Data and distributed systems, you must drastically simplify your approach. This book introduces a general framework for thinking about big data, and then shows how to apply technologies like Hadoop, Thrift, and various NoSQL databases to build simple, robust, and efficient systems to handle it.