Big Data: Principles and best practices of scalable realtime data systems, Nathan Marz, James Warren

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

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