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Big Data Glossary [Paperback]

Pete Warden
5.0 out of 5 stars  See all reviews (1 customer review)
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Price: 11.65 & FREE Delivery in the UK. Details
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Book Description

25 Sep 2011 1449314597 978-1449314590 1

To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment.

This handy glossary also includes a chapter of key terms that help define many of these tool categories:

  • NoSQL Databases—Document-oriented databases using a key/value interface rather than SQL
  • MapReduce—Tools that support distributed computing on large datasets
  • Storage—Technologies for storing data in a distributed way
  • Servers—Ways to rent computing power on remote machines
  • Processing—Tools for extracting valuable information from large datasets
  • Natural Language Processing—Methods for extracting information from human-created text
  • Machine Learning—Tools that automatically perform data analyses, based on results of a one-off analysis
  • Visualization—Applications that present meaningful data graphically
  • Acquisition—Techniques for cleaning up messy public data sources
  • Serialization—Methods to convert data structure or object state into a storable format

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Big Data Glossary + Human Face of Big Data, The + Big Data Analytics: Turning Big Data into Big Money (Wiley and SAS Business Series)
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Product details

  • Paperback: 62 pages
  • Publisher: O'Reilly Media; 1 edition (25 Sep 2011)
  • Language: English
  • ISBN-10: 1449314597
  • ISBN-13: 978-1449314590
  • Product Dimensions: 0.3 x 17.8 x 22.9 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 980,979 in Books (See Top 100 in Books)
  • See Complete Table of Contents

More About the Author

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Product Description

Book Description

A Guide to the New Generation of Data Tools

About the Author

A former Apple engineer, Pete Warden is the founder of OpenHeatMap, and writes on large-scale data processing and visualization.

Customer Reviews

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Most Helpful Customer Reviews
4 of 4 people found the following review helpful
5.0 out of 5 stars A good starting book about big data 23 Oct 2011
Format:Kindle Edition
This book is good starting point to who have to deal with big data, with more than sixty tools described in less than fifty pages.
As a glossary is supposed to be, each term is not described in deep, but the book reports some hints about similar tools and suggests when you may found useful explore that tool.

Experienced people may found the description of a well know term too brief, but the glossary is so huge that they can found new tools to investigate.

In my opinion the book lacks of a complete references list, but a short internet search may set aside that defect.
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Most Helpful Customer Reviews on (beta) 2.7 out of 5 stars  6 reviews
5 of 5 people found the following review helpful
1.0 out of 5 stars What a waste! 3 Dec 2011
By Thomas - Published on
Format:Paperback|Verified Purchase
I am not sure what it took for a respectful brand like O'Reilly's to waste its reputation on a series of books that would frankly make Gutenberg roll over in his grave. This is yet another one of those "Let's get it out to market quickly so we don't waste the latest buzzword wave" books. Tim, please stop this! Blogs, Twitter, and Facebook are the medium for short attention span literature. The printed word is not!
2 of 2 people found the following review helpful
2.0 out of 5 stars I won't recommend this 24 April 2012
By Jayakumar Gopalan - Published on
First of all, the content of this book is a good candidate for an article / blog * definitely NOT for a book that too when you expect to pay $20 * Most of us won't expect this kind of book where you can get better information from wikipedia about these buzz words just following links from big data [...]

However, they didn't cheat me and they mentioned the title as Big Data Glossary and amazon mentioned the number of pages as 62, so I shouldn't have expect more content in it. I have to write-off this ~ $20.

I didn't mean to promote this book, but if you are interested in Big Data look out for Manning's Big Data by Nathan Marz @nathanmarz (it is in MEAP / early access / rough cuts stage and I paid the MEAP and as of this writing Nathan set out three chapters) and it clearly explains the why, when, what, how of Big Data (so far in the three chapters why and when was laid out very clearly waiting for other chapters for what and how). Sorry if I talked too much about Nathan's book but I couldn't stop typing about it.
1 of 1 people found the following review helpful
5.0 out of 5 stars Delivers what it claims for 6 Mar 2012
By Dan InGold - Published on
If you are new to the field and want to have a general overview of Big data tools within 2 hours, this is a great book for you. From here, you will stay in touch with the most recent technologies. This book will point you to many resources. If you are already in the field, this is not a book for you and there is no reason that this book will catch your eye in the first place.
1 of 1 people found the following review helpful
3.0 out of 5 stars Be Careful! 24 Feb 2012
By Paco - Published on
The title of the book is "Big Data Glossary" and the listing here on Amazon clearly indicates it's only 60 pages. If you're ordering this book to learn all about the avalanche of big data technologies.... you will not be happy. (It only takes an hour to read.)

If, on the other hand, you want a quick read to understand the basic concepts behind many of the hot big-data technologies, this would probably be a good read... but remember that it's only a glossary. Is it worth $20? Not my $20.... I read it on Safari.
1 of 1 people found the following review helpful
3.0 out of 5 stars Not much to read 11 Dec 2011
By FIRAT ATAGUN - Published on
Yes, it s a short book and there is literally nothing worth to read. Just introduction to products what they are and a paragraph for each within some classification. might be useful for someone who is engaging to big data.
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