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Big data [electronic book] : principles and best practices of scalable real-time data systems / Nathan Marz ; with James Warren.

By: Marz, Nathan [author.].
Contributor(s): Warren, James (Software engineer) [contributor.].
Material type: materialTypeLabelBookPublisher: Shelter Island, New York : Manning, [2015]Copyright date: ©2015Description: 1 online resource (xx, 308 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9781638351108 .Subject(s): Big data | Database management | Database design | Data miningAdditional physical formats: Print version: Big data : principles and best practices of scalable real-time data systemsDDC classification: 658.4038 Online resources: Access eBook Also available in print form
Contents:
A new paradigm for big data -- 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 -- Serving layer -- Serving layer : illustration -- 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.
Holdings
Item type Current library Call number Status Notes
eBook MTU Online eBook 658.4038 (Browse shelf(Opens below)) Available (Online) Kerry Module DBMS81008
Total holds: 0

Enhanced descriptions from Syndetics:

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.

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

Includes index.

A new paradigm for big data -- 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 -- Serving layer -- Serving layer : illustration -- 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.

Also available in print form

Electronic reproduction: ProQuest LibCentral. Mode of Access: World Wide Web

Electronic reproduction. Ann Arbor, MI : ProQuest, 2018. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

Author notes provided by Syndetics

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

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 sup