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Big Data: Principles and best practices of scalable realtime data systems
PDF Download Big Data: Principles and best practices of scalable realtime data systems
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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.
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Product details
Paperback: 328 pages
Publisher: Manning Publications; 1 edition (May 10, 2015)
Language: English
ISBN-10: 1617290343
ISBN-13: 978-1617290343
Product Dimensions:
7.3 x 0.6 x 9.1 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
37 customer reviews
Amazon Best Sellers Rank:
#58,029 in Books (See Top 100 in Books)
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 data2. 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.
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.
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.
Good theoretical review of Big Data architecture. Not so great for implementation details using current frameworks.
Gave me a good idea of what Big Data actually is, and how it's implemented. Another tool in the chest.
nice
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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