> On 25 Jun 2015, at 17:10, Ihe Onwuka <[email protected]> wrote: > > > On Wed, Jun 24, 2015 at 12:30 PM, Pavel Velikhov <[email protected] > <mailto:[email protected]>> wrote: > > A lot of people are content with MongoDB to store the JSONs. So a killer > use-case needs to look beyond dumb storage of JSONs. Maybe focus on the > preparation/transformation/cleaning/merging stuff. > > > > > > But the biggest factor was probably that the move to minicomputer > > architecture created a discontinuity that forced people to consider change. > > You need to do two things: convince people that the new technology is > > better (or at least, is cool), and give them a big kick up the backside to > > get them out of their comfort zone. > > > > Michael Kay > > Saxonica > > > > The data prep/transformation/cleaning/merging stuff is currently the domain > of R and Python.
You must be talking about “data science” that is used internally in the organization. I’m talking more about data-driven Web sites, that have a big data component in their products. In this case folks would never use R, they use all sorts of other stuff, including Python. > > R because thats what the statisticians like and (if you will see if you watch > the R Good Bad and Ugly presentation I posted) they are not going to change. > Unfortunately they are being sheepishly followed by non-statisticians. The > non-statisticians who could change this - the software people - are for the > most part saying I don't care if R sucks for data management and I don't > care that I am not a statistician, working with R will help me get a sexy > data science job. QED. > > With Python you have the same issue but with the additional twist that it is > revered for being Swiss Army knife for devs and data scientists. This is > another one of those situations where the industry inverts common sense and > transforms what should ordinarily be a handicap into a virtue. > > Ok so you go to the restaurant, place your order and they bring your food. > How many of you are now going to reach into your pocket and eat it with this. > > http://gadgether.walyou.netdna-cdn.com/wp-content/uploads/2009/11/swissarmius-main-01.jpg > > <http://gadgether.walyou.netdna-cdn.com/wp-content/uploads/2009/11/swissarmius-main-01.jpg> > > So there is a very challenging people issue to overcome > > Technically there would need to be a streaming capability so that > XQuery/JSONiq is not the part of the pipeline that barfs when fed a large > dataset. We’re thinking about building a JSONiq component in Scala, so it could be plugged into Spark. > > > С уважением, Павел Велихов [email protected]
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