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Bibliographic Details

Title: Big Data: Principles and best practices of scalable realtime data systems
Publisher: Manning Publications
Publication Date: 2015
Language: English
ISBN 10: 1617290343
ISBN 13: 9781617290343
Binding: Paperback
Condition: Very Good

About this title

Synopsis

Summary

Big Data teaches you to build big data 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.

What's Inside

Introduction to big data systems
Real-time processing of web-scale data
Tools like Hadoop, Cassandra, and Storm
Extensions to traditional database skills

About the Authors

Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing.

Table of Contents

A new paradigm for Big Data

PART 1 BATCH LAYER

Data model for Big Data
Data model for Big Data: Illustration
Data storage on the batch layer
Data storage on the batch layer: Illustration
Batch layer
Batch layer: Illustration
An example batch layer: Architecture and algorithms
An example batch layer: Implementation

PART 2 SERVING LAYER

Serving layer
Serving layer: Illustration

PART 3 SPEED LAYER

Realtime views
Realtime views: Illustration
Queuing and stream processing
Queuing and stream processing: Illustration
Micro-batch stream processing
Micro-batch stream processing: Illustration
Lambda Architecture in depth

About the Author

Nathan Marz is currently working on a new startup. Previously, he was the lead engineer at BackType before being acquired by Twitter in 2011. At Twitter, he started the streaming compute team which provides and develops shared infrastructure to support many critical realtime applications throughout the company. Nathan is the creator of Cascalog and Storm, open-source projects which are relied upon by over 50 companies around the world, including Yahoo!, Twitter, Groupon, The Weather Channel, Taobao, and many more companies.

James Warren is an analytics architect at Storm8 with a background in big data processing, machine learning and scientific computing.

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