Big Data: Principles and best practices of scalable realtime data systems 1st Edition

by Nathan Marz (Author), James Warren (Author)

36 customer reviews

ISBN-13: 978-1617290343

ISBN-10: 9781617290343

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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

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From the Publisher

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About This Book

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling Big Data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you just need to take a different approach. Big Data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

Big Data teaches you to build these 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 and how to implement them in practice.

Big Data requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful, though not required. The goal of the book is to teach you how to think about data systems and how to break down difficult problems into simple solutions. We start from first principles and from those deduce the necessary properties for each component of an architecture.

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.

Paperback: 328 pages
Publisher: Manning Publications; 1 edition (May 10, 2015)
Language: English
ISBN-10: 9781617290343
ISBN-13: 978-1617290343
ASIN: 1617290343
Product Dimensions: 7.3 x 0.6 x 9.1 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review: 36 customer reviews
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Amazon Customer

If you are looking for a survey of different approaches ...

April 10, 2016

Format: PaperbackVerified Purchase

If you are looking for a survey of different approaches of handling big data, you want to read "ELEMENTS OF SCALE: COMPOSING AND SCALING DATA PLATFORMS". ([...]) This book is dedicated to Lambda Architecture (one that is surveyed in the above article.)

The book is very organized. Introduction in chapter 1 will be the road map of the whole book. Motivating with a simple web application based on RDBMS, the author showed how the approach to scale it becomes undesirable. After enumerating a list of desired properties, he proposed Lambda architecture, an approach in contrast to fully incremental architecture (with RDBMS).

The Lambda architecture is partitioned into three layers:
1. batch layer that computes different views on big data
2. serving layer that answers user queries using views from the batch layer and speed layer.
3. speed layer that compensates an approximate answer over a period time when the batch layer is working on the complete answers.

In the remaining chapters, the author dive deep into the rationale and requirements of all the different pieces of Lambda Architecture.

To under the context of Lambda Architecture, also refer to the wikipedia for crticism.

7 people found this helpful

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Dimitri K

Written by a specialis

March 12, 2016

Format: PaperbackVerified Purchase

This book is written by a specialist in big data. I know that because I worked on the big data pipeline. And now I read the book and I see that all my problems are addressed in this book. Virtually every problem discussed appeared in my pipeline too, as if the author worked with me on my project.

The other very useful for me feature of this book is that it is the first book where I could find a concise explanation of Storm Trident framework, even though the book is not about Storm.

6 people found this helpful

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Swami

Nice source of 'things to consider/be aware of' though

September 9, 2016

Format: PaperbackVerified Purchase

Basically a sell of Lambda Architecture. A bit dated to use for proposed architecture since there many new design patterns now that get around some of the limitations. Nice source of 'things to consider/be aware of' though.

2 people found this helpful

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Robert

Lambda

October 21, 2016

Format: PaperbackVerified Purchase

Good theoretical review of Big Data architecture. Not so great for implementation details using current frameworks.

2 people found this helpful

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Timothy W. Smith

Gave me a good idea of what Big Data actually is

July 2, 2017

Format: PaperbackVerified Purchase

Gave me a good idea of what Big Data actually is, and how it's implemented. Another tool in the chest.

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fontis

Five Stars

July 4, 2018

Format: PaperbackVerified Purchase

nice

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Amazon Customer

Insight into the lambda architecture

March 11, 2017

Format: PaperbackVerified Purchase

This book serves as a guide to building the lambda architecture from scratch, processing and serving big data. It is not a book to teach you about big data technologies though. If you are looking to learn and implement a system with batch and real time layers, this is the best book there is.

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Zambonilli

Lambda Architecture FTW

June 14, 2015

Format: PaperbackVerified Purchase

Great explanation of both the theory and practice of the lambda architecture. While the practice chapters are nice, it's the theory chapters that really shine. The book explains down to the byte level why components are implemented the way they are. For example, there's an immense amount of detail as to why using a db that doesn't support random writes allows for an application to query the batch layer's results without locking.

The only downside to the book is that the architecture and exosystem is so new that there's not really a lot of pragmatic solutions. For example, the theory describes a query layer that can merge the results of batch and real time processing for client applications. However, in real life there are no pragmatic solutions for doing this so you'd have to write your own.

It'll be interesting to see how the lambda architecture matures and to see future editions of this book. Hopefully, future editions will be as well written and have a better ecosystem for practice chapters.

15 people found this helpful

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