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

Format:

Book

Author/Creator:

Marz, Nathan, author.

Contributor:

Warren, James (James O.), 1974- contributor.

Language:

English

Subjects (All):

Big data.

Database management.

Database design.

Data mining.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Other Title:

Principles and best practices of scalable real-time data systems

Place of Publication:

Shelter Island, New York : Manning, [2015]

Language Note:

English

System Details:

text file

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.

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.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781638351108

1638351104

9781617290343

1617290343

OCLC:

911057816