- Tapa blanda: 386 páginas
- Editor: O'Reilly Media; Edición: 1 (31 de julio de 2009)
- Idioma: Inglés
- ISBN-10: 0596157118
- ISBN-13: 978-0596157111
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
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- n.° 2637 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Bases de datos
- n.° 4521 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Software y aplicaciones de negocio
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Beautiful Data: The Stories Behind Elegant Data Solutions (Inglés) Tapa blanda – 31 jul 2009
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Reseña del editor
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.
With Beautiful Data, you will:
- Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
- Learn how to visualize trends in urban crime, using maps and data mashups
- Discover the challenges of designing a data processing system that works within the constraints of space travel
- Learn how crowdsourcing and transparency have combined to advance the state of drug research
- Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
- Learn about the massive infrastructure required to create, capture, and process DNA data
That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
Biografía del autor
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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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.
Chapter 5 by Jeff Hammerbacher really didn't offer too many insights but his writing style is fluid and fun plus he offered a glimpse into how Facebook grew.
We then have the slow section of the book - Chapter 8 on distributed social data had promise but it read more like a company white page than an interesting article. Same with Chapter 12 [...].
Thankfully chapter 10 on Radiohead's "House of Cards" video was there - and here we are presented with true beauty in data - beautiful enough to create a music video out of!
I'm still on the fence with Chapter 13 - What Data Doesn't Do. It was an interesting chapter but it felt both too long and too short at the same time. I almost felt that in the author, Coco Krumme, were to write a book on this topic, I'd want to read it. However her essay was not the right vehicle.
Finally, the last chapter - "Connecting Data" was a truly inspiring piece; one that offers up paths for the future. I am sure a few start ups will form over the questions posed in by Segaran (or maybe the questions to the questions).
Overall there were enough strengths to overcome the weak chapters. My main complaints are trivial; poor binding of the book, too many PhD candidate papers and not enough from out in the trenches. I'd love to see something from Stonebreaker here; its hard to talk about beautiful data and not have him in it. Or forget [...]and talk about many eyes. Or map reduce. Still, "Beautiful Data" succeeds. It opened up my mind to different possibilities for data representation and usage.
Since this is a collection of "stories" some of them are very interesting e.g. about Census data, other were in my opinion somehow less relevant.
It is definitely inspirational and it is a good guide to see what other people were able to achieve through data mining. You might find that somebody already solved a problem similar to yours.