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Big Data Now: Current Perspectives from O'Reilly Radar
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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 co
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As much as I like reading about technical stuff, this book was unsatisfying.
Admittedly, this book is a bit out of date now. And everyone is very earnest, and clearly thinking hard in this book. But this is roughly how each essay felt:
Person talks about how data science is important in [CHOSEN TOPIC]. Then, how [OBVIOUS PROBLEM A] is surprisingly related to [OBVIOUS PROBLEM B] and so after some thought, is something that deserved more attention and [NEW STARTUP] is specializing in [MONETIZING THE ...more
Admittedly, this book is a bit out of date now. And everyone is very earnest, and clearly thinking hard in this book. But this is roughly how each essay felt:
Person talks about how data science is important in [CHOSEN TOPIC]. Then, how [OBVIOUS PROBLEM A] is surprisingly related to [OBVIOUS PROBLEM B] and so after some thought, is something that deserved more attention and [NEW STARTUP] is specializing in [MONETIZING THE ...more
Aug 20, 2013
Richard Magahiz
rated it
liked it
Recommends it for:
data scientists
Shelves:
nonfiction,
read-in-2013
Series of short form essays and interviews circa 2010, still relevant. Most of the links still work at the current time, which is worth something. It is a mix of big picture and details of implementations which may no longer be current, discussed at a level where one does not need to be an insider to understand the gist of the subjects. It served as a useful guide to the architecture behind the largest fixtures in the Big Data space though of course it leaves out the ones who have only made a na
...more
This, although a bit dated, is a really good primer into the terminology, challenges and excitement of "big data". It's available as a free PDF download. There are a lot of adverts in amongst the text, and they're all for a conference that has already been and gone. Whilst annoying, it was a free download, so I don't feel I can really complain. I now subscribe to a filtered O'Reilly Radar feed looking for big data related articles to continue my learning
Interesting series of interviews and essays about what big data holds in store for people. It was bit fragmented, and some essays were really jargony, but perhaps the state of big data itself: no one is quite sure whats going on. In some sense though, it's a perfect way to understand what is going on in this rapidly evolving concept.
This book contains tons of interesting information, interviews, opinions,questions about big data and companies and people already involved in development. For some people this is a dated information, but for me it was very informative.
A mixed bag of essays on Big Data. Some of the technical ones were interesting and I learned new things. Some of the case studies and interviews with people in the Big Data product area were less interesting.
Pretty straight forward and simple to read if you want to have a general idea and intriduction about Big Data
Read the first 1/3 if you don't already know the jargon.
An interesting collection of topics and interviews. My favorite was a reminder how useful some UNIX commandline tools still are. I learned a couple of new ones, to go along with my old favorites.
a collection of articles which talk about diverse topics about Big Data from definitions, concepts, elements, difficulties, current startup examples and thoughts about the future of Big Data
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“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
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“Making data tell its story isn’t just a matter of presenting results; it involves making connections, then going back to other data sources to verify them.”
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