Big Data: Principles and best practices of scalable realtime data systems

Nathan Marz; James Warren

ISBN 10: 1617290343 / ISBN 13: 9781617290343

Published by Manning, 2015

About this Item

The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact (including the dust cover, if applicable). Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials. Seller Inventory # 4WILKM00M4BQ

Bibliographic Details

Title: Big Data: Principles and best practices of ...

Publisher: Manning

Publication Date: 2015

Binding: Soft cover

Condition: 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

1. A new paradigm for Big Data

PART 1 BATCH LAYER

2. Data model for Big Data
3. Data model for Big Data: Illustration
4. Data storage on the batch layer
5. Data storage on the batch layer: Illustration
6. Batch layer
7. Batch layer: Illustration
8. An example batch layer: Architecture and algorithms
9. An example batch layer: Implementation

PART 2 SERVING LAYER

10. Serving layer
11. Serving layer: Illustration

PART 3 SPEED LAYER

12. Realtime views
13. Realtime views: Illustration
14. Queuing and stream processing
15. Queuing and stream processing: Illustration
16. Micro-batch stream processing
17. Micro-batch stream processing: Illustration
18. 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.