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Beautiful Data: The Stories Behind Elegant Data Solutions Paperback – July 31, 2009

ISBN-13: 978-0596157111 ISBN-10: 0596157118 Edition: 1st

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Product Details

  • Paperback: 386 pages
  • Publisher: O'Reilly Media; 1 edition (July 31, 2009)
  • Language: English
  • ISBN-10: 0596157118
  • ISBN-13: 978-0596157111
  • Product Dimensions: 0.8 x 9 x 6.7 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #290,474 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

Toby Segaran is the author of Programming Collective Intelligence, a very popular O'Reilly title. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.

Jeff Hammerbacher is the Vice President of Products and Chief Scientist at Cloudera. Jeff was an Entrepreneur in Residence at Accel Partners immediately prior to joining Cloudera. Before Accel, he conceived, built, and led the Data team at Facebook. The Data team was responsible for driving many of the statistics and machine learning applications at Facebook, as well as building out the infrastructure to support these tasks for massive data sets. The team produced several academic papers and two open source projects: Hive, a system for offline analysis built above Hadoop, and Cassandra, a structured storage system on a P2P network. Before joining Facebook, Jeff was a quantitative analyst on Wall Street. Jeff earned his Bachelor's Degree in Mathematics from Harvard University.


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Customer Reviews

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This book was, to me, truly extraordinary and truly entertaining.
Michael McCown
I like that I was able to skip around and not have to read the book cover to cover, which I do not have the patience to do with any tech book.
David Chin
This book is very entertaining if you are interested in data analysis, mining and visualization.
Oscar Cassetti

Most Helpful Customer Reviews

52 of 52 people found the following review helpful By Amazon Customer on October 11, 2009
Format: Paperback Verified Purchase
"Beautiful Data" is a collection of essays on data; how people have transformed it, worked within its confines, and offers a glimpse of where we might go. Many of the essays are wonderful snippets into how some people perceive data while others fall flat. Overall its a mostly enjoyable read that helps open up your mind to new potentials.

First a disclaimer; I am not a data person. However I've been involved, fairly heavily, in the data field. In the parlance of the world, I'm a back end person. However I'm always trying to think about the front end; how will things be used and what information can we gleen from the system (or systems). With that in mind, this is a book that speaks to me - its all about the front end.

Some of the best essays in the book would be:

The first essay by Nathan Yau he talks very much about user created data and personal databases (knowledge bases). What's exciting here is how he takes data already out there, data you have provided, and creates something useful and yes, beautiful, out of it.

The Second essay by Follett and Holm really gets down to how if you want the data, you need to present it in a way that brings people into the process. As someone who has a slight crush on the statistics and practices in polling (and designing poll questions) this essay really was a fascinating read.

The third essay by Hughes detailed how he handled images on the Mars mission. There wasn't anything here that wasn't done in embedded systems 15 years ago; still it was a great walk down memory lane since I used to program embedded imaging systems.

Chapter 4 really hit home PNUTShell is cloud storage and data processing in real time. This really is the stuff of the future.
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33 of 33 people found the following review helpful By Dimitri Shvorob on November 8, 2009
Format: Paperback
... Contents are less impressive. O'Reilly bring together a heterogeneous group of authors and let them fend for themselves, with no editorial effort to unite their stories. Some authors hold their own, presenting interesting analyses and visualizations, or just interesting tales, others are less successful. (The spectrum of statistical expertise, for example, is bounded by Andrew Gelman and a graduate student believing that normality is a requirement of the central-limit theorem). 'Interesting' is a good thing, but for $40 I would like 'useful'. An appealing leisure read, but not much more, I am afraid.
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Format: Paperback
This book tells you what's possible now and what's on the horizon when it comes to data representation, collection, management, processing, analysis, sharing, and display. Very little code is provided because each chapter is mostly a conceptual discussion of approaches to tackling various kinds of challenges involving data, the lifeblood of any application. My favorite chapters are: 4, 5, 7 and 20. Below are my short notes for each chapter to give you some idea of the book's contents.

Ch. 1 Seeing Your Life in Data by Nathan Yau
Hoping to better understand their impact on and exposure to the environment, participants in one of Yau's projects download software onto their phones that then upload GPS data to servers as they go about their daily activities. One of Yau's early challenges was to summarize the data and make it meaningful to the participants: for example, what does it mean to emit 1,000 kilograms of carbon in a week? What he found helpful and not so helpful in data visualization are instructive.

Ch. 2 The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods by Jonathan Follett and Matthew Holm
When there is no explicit profit to be made, how do you convince a person to take the time to answer your survey questions?

Ch. 3 Embedded Image Data Processing on Mars by J.M. Hughes
Like everything else onboard a spacecraft, the computing system is custom built with minimalism and other stringent specifications (e.g., withstand radiation) in mind. How does one harness limited resources to get the job done?

Ch. 4 Cloud Storage Design in a PNUTShell by Brian Cooper, Raghu Ramakrishnan, and Utkarsh Srivastava
Yahoo! engineers have a very challenging job.
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7 of 7 people found the following review helpful By hvu on March 30, 2011
Format: Paperback
Most of the stuff here really didn't hold my interest, I was looking something more closely related to practical engineering work. But it does have a nice looking cover.
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20 of 25 people found the following review helpful By Thomas W. Gonzalez on September 1, 2009
Format: Paperback Verified Purchase
While the content of this book is interesting and informative, I am struck with what lousy print quality it is. For a $40+ book you would expect a hardback, or at least a paperback with thick stock pages and color plates that actually look good. It was hard for me to appreciate the content when it felt like each page (or the cover) was going to rip because they were such thin and poor quality stock. The color plates are washed out and pixelated. I was expecting the same high quality we got with "Beautiful Code". O'Reilly usually does a much better job. That said, if these types of aesthetics don't bother you (although with a title like "Beautiful Data" I would question that it wouldn't) the book itself is an interesting read.
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3 of 3 people found the following review helpful By Shawn Day on December 20, 2010
Format: Paperback
Beautiful Data is a collection of essays on exploring the organisation, manipulation and display of data in 'beautiful way'. The editors, Toby Segaran and Jeff Hammerbacher, have attempted to loosely organise the papers into logical process of: collection --> storage --> organisation --> retrieval --> visualisation --> analysis and in theory this works. The challenge as with any collection of papers from such a diverse set of authors (39 in this case) is finding that common thread that flows through the works. In this the editors achieve a passing grade, but frankly, this is sort of the book that offers the reader something they will find useful, but only due to the breadth of articles included. The downside is that there will certainly be articles that a reader will not. The authors seem to realise this and use the term 'loose' with some frequency. But I can't criticise this and would want to. This is a strength of the book. It covers much ground and will appeal to many.

Conceptually, the demand for a book in this area is huge. Having delivered a number of workshops in this area and been asked to adjudicate on conference papers in the past two years, I am certainly aware of breadth, and the demand for skills and knowledge in this broad area.

The first article by Nathan Yau, builds from his popular blog posts on flowingdata.com and provides more depth on two case studies involving the collection, analysis and visualisation of data gathered from going about your own life. He is painting a picture of life to come as more of our life becomes monitored and we are raised to a new level of consciousness of how we live. His article explores how we might internalise the analysis of this data and how it could impact on life activities.
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