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Big Data: Principles and best practices of scalable realtime data systems, by Nathan Marz, James Warren
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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
- Sales Rank: #25989 in Books
- Brand: Marz, Nathan/ Warren, James
- Published on: 2015-05-10
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x .60" w x 7.30" l, .0 pounds
- Binding: Paperback
- 328 pages
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.
Most helpful customer reviews
25 of 26 people found the following review helpful.
Other books in this area tend to focus a lot more on the "gee whiz" coolness of data science and machine learning applications (
By Kirk D. Borne
I have rarely seen a thorough discussion of the importance of data modeling, data layers, data processing requirements analysis, and data architecture and storage implementation issues (along with other "traditional" database concepts) in the context of big data. This book delivers a refreshing comprehensive solution to that deficiency. Other books in this area tend to focus a lot more on the "gee whiz" coolness of data science and machine learning applications (which are aspects of big data that I happen to love, but they are not the whole story). You cannot hope to achieve good, effective, and efficient results from your analytics processes without good data flow, from discovery to access to integration, which is why architecture design, data modeling, and attention to data pipelining are essential. I highly recommend this book for anyone who isn't ashamed to admit that data engineering is at least as important as data science in the big data era (says this data scientist!).
13 of 14 people found the following review helpful.
A clear-eyed look at good ways to keep your Big Data system from becoming overwhelmed by complexity and volume
By Si Dunn
Here's my bottom line: Get this book, whether you are new to working with Big Data or now an old hand at dealing with Big Data’s seemingly never-ending (and steadily expanding) complexities.
You may not agree with all that the authors offer or contend in this well-written "theory" text. But Nathan Marz’s Lambda Architecture is well worth serious consideration, especially if you are now trying to come up with more reliable and more efficient approaches to processing and mining Big Data. The writers' explanations of some of the power, problems, and possibilities of Big Data systems are among the clearest and best I have read.
"More than 30,000 gigabytes of data are generated every second, and the rate of data creation is only accelerating," Marz and Warren point out.
Thus, previous "solutions" for working with Big Data are now getting overwhelmed, not only by the sheer volume of information pouring in but by greater system complexities and failures of overworked hardware that now plague many outmoded systems.
The authors have structured their book to show "how to approach building a solution to any Big Data problem. The principles you’ll learn hold true regardless of the tooling in the current landscape, and you can use these principles to rigorously choose what tools are appropriate for your application.” In other words, they write, you will “learn how to fish, not just how to use a particular fishing rod.”
However, a particular Big Data architecture IS featured, as well: Marz's Lambda Architecture. It is, the two authors explain, "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."
The Lambda Architecture has three layers: the batch layer, the serving layer, and the speed layer.
Not surprisingly, the book likewise is divided into three parts, each focusing on one of the layers:
In Part 1, chapters 4 through 9 deal with various aspects of the batch layer, such as building a batch layer from end to end and implementing an example batch layer.
Part 2 has two chapters that zero in on the serving layer. "The serving layer consists of databases that index and serve the results of the batch layer," the writers explain. "Part 2 is short because databases that don’t require random writes are extraordinarily simple.”
In Part 3, chapters 12 through 17 explore and explain the Lambda Architecture’s speed layer, which “compensates for the high latency of the batch layer to enable up-to-date results for queries.”
Marz and Warren contend that "[t]he benefits of data systems built using the Lambda Architecture go beyond just scaling. Because your system will be able to handle much larger amounts of data, you’ll be able to collect even more data and get more value out of it. Increasing the amount and types of data you store will lead to more opportunities to mine your data, produce analytics, and build new applications."
This book requires no previous experience with large-scale data analysis, nor with NoSQL tools. However, it helps to be somewhat familiar with traditional databases. Nathan Marz is the creator of Apache Storm and originator of the Lambda Architecture. James Warren is an analytics architect with a background in machine learning and scientific computing.
(My thanks to Manning for providing a review copy of this book.)
12 of 14 people found the following review helpful.
Lambda Architecture FTW
By Zambonilli
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.
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