This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas:
Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself.
The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research.
Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.
The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.
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Most Helpful Customer Reviews
4 of 5 people found the following review helpful
Format:Kindle Edition
Thorough introduction to the current Big Data landscape. After discussions of data science, associated tooling used in this space, and issues likely to be encountered with data, which comprise about half the text, the editors shift the discussion to the application of data science findings and the business of data. The only dissenter in terms of the effectiveness of what the authors share here actually touches upon a benefit rather than a drawback when they write that this book was apparently not written by data scientists: because the authors write with management in mind, the discussion does not get lost in the many technical details that comprise Big Data, but instead provides a compact, highly readable summary of a range of related subject matter.
The authors tackle Big Data terminology well, beginning with the term "Big Data" itself. "We've all heard a lot about 'big data' but 'big' is really a red herring. Oil companies, telecommunication companies, and other data-centric industries have had huge datasets for a long time. And as storage capacity continues to expand, today's 'big' is certainly tomorrow's 'medium' and next week's 'small'. The most meaningful definition I've heard: 'big data' is when the size of the data itself becomes part of the problem. We're discussing data problems ranging from gigabytes to petabytes of data. At some point, traditional techniques for working with data run out of steam." The aspect that makes what is now being attempted different is that information platforms designed to explore and understand data, beyond traditional business intelligence, are being built.
As a consultant, I especially appreciate the links that the editors provide throughout for tooling and other technical subjects that would otherwise significantly increase the length of this white paper sized book. The interviews with individuals from companies in this space, such as Infochimps and Gnip, is also appreciated. And although it ends rather abruptly, the last chapter on the business of data, which compliments the first chapter, is especially well done, including a discussion of the emerging Big Data stack comprised of both open source and commercial products that furthers the presention of the SMAQ stack (Storage, MapReduce, and Query) discussed earlier in the book. Recommended to anyone within or looking to enter the Big Data space.
The authors tackle Big Data terminology well, beginning with the term "Big Data" itself. "We've all heard a lot about 'big data' but 'big' is really a red herring. Oil companies, telecommunication companies, and other data-centric industries have had huge datasets for a long time. And as storage capacity continues to expand, today's 'big' is certainly tomorrow's 'medium' and next week's 'small'. The most meaningful definition I've heard: 'big data' is when the size of the data itself becomes part of the problem. We're discussing data problems ranging from gigabytes to petabytes of data. At some point, traditional techniques for working with data run out of steam." The aspect that makes what is now being attempted different is that information platforms designed to explore and understand data, beyond traditional business intelligence, are being built.
As a consultant, I especially appreciate the links that the editors provide throughout for tooling and other technical subjects that would otherwise significantly increase the length of this white paper sized book. The interviews with individuals from companies in this space, such as Infochimps and Gnip, is also appreciated. And although it ends rather abruptly, the last chapter on the business of data, which compliments the first chapter, is especially well done, including a discussion of the emerging Big Data stack comprised of both open source and commercial products that furthers the presention of the SMAQ stack (Storage, MapReduce, and Query) discussed earlier in the book. Recommended to anyone within or looking to enter the Big Data space.
By Steve Lee
Format:Kindle Edition|Amazon Verified Purchase
This is a nicely compiled book that gives you a general introduction to big data. Easy to read. Has a lot of good interviews.
Format:Kindle Edition
May as well be an advert for Gnip though. I was hoping for more technical depth, but this is mostly media interviews.
Most Recent Customer Reviews
4.0 out of 5 stars
Great for Free, Chapters on Different Subjects
This has two or three chapters that do a good job of explaining what Big Data is, and what the tools are to work with it. That was exactly what I was looking for. Read more
Published 1 month ago by E. Walker
4.0 out of 5 stars
Different interesting viewpoints, a bit repetitive
I liked the book because it gave me different perspectives on Big Data from people at different places on the big data stack. Read more
Published 1 month ago by V. Fernandes
4.0 out of 5 stars
Solid intro to the field
This is by no means a definitive guide to operating in this field, but is a very good framework for helping figure out how the vastly different technologies of Big Data can help... Read more
Published 1 month ago by Frank D. Evans
4.0 out of 5 stars
Nice overview
This book gives an excellent bird's view and description them would be helpful for and head up start. Also references are very helpful
Published 2 months ago by rudra
4.0 out of 5 stars
Great skimming material for Big Data
This is a collection of essays / magazine-like articles. Some of the articles have useful information, while others seem a little more like fluff.
Published 3 months ago by Mike D
4.0 out of 5 stars
worth reading for a free bk on kindle
good book. a b c d e f g h i j k l m n o p q r s t
Published 4 months ago by G. Balasubramaniam
3.0 out of 5 stars
Worth the money
Got this to prepare for a meeting with some big data folks. It was not exactly a fascinating read but a pretty good compendium of big data related stuff.
Published 5 months ago by Brian
4.0 out of 5 stars
good overview of what big data actually means
If you are familiar with Big Data, Hadoop, and similar technologies, then this is not the book for you. Read more
Published 5 months ago by nobody
4.0 out of 5 stars
Good Primer
Big data is all the rage these days and only few people are qualified to actually do it. This is a good primer to see what are current challenges.
Published 6 months ago by Jowanza
4.0 out of 5 stars
Brief review of big data software
This text is a very good compilation of different kind of software that manages bigdata. It was very interesting to read it, just to know how many products are helping now to deal... Read more
Published 7 months ago by Javier Szmajser
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