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How to help your teams to leverage data by using the perfect graphs.

We’ve written posts about retrieving data, and storing and using data. Us and a lot of other people. These days, it’s all about the data. We process +100 TB of data per day, feeding a machine-learning algorithm that allows us to drive incremental sales for app marketers. Data makes the performance marketing world go round. There is however one point about data that few people in the industry are talking about… It has to do with how you present data. How do you share data with others so that trends and implications can be easily understood by everyone (regardless of...
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#python

An in depth look into caching (Part 2)

…we posted about caching. Time to post about caching again. This time, we’re going to concentrate on the complexities of caching at scale. You know, you have a big honking piece of a datacenter crunching numbers so you can post the most awesome kitty pics. That’s what the internet is for after all. And you want your awesome kitty pics to appear in less than 100 milliseconds because… well, teenagers are impatient.
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#data-science #python

How we use Jupyter + Airpal to improve our Data Analytics processes

Being a data driven company, reporting needs are constantly increasing in Jampp. From basic summarizations to complex analysis, every team needs to query our databases. Given this backdrop, a priority for our tech team is to readily provide these reports to non-technical areas. Client-sided and other frequently used reports can be found on our Dashboard . Initially, this was enough to cover Jampp’s evolving reporting needs but, for some time now, we found ourselves getting more and more report and visualizations requests.
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#python

An in depth look into caching (Part 1)

Nobody calls it that way anymore. But the term is oddly descriptive. Nowadays, it’s all about interconnected systems. You log into your mobile game with your Google account, or maybe your Facebook account. You search for some page (of which you never ever knew its address - honestly, who types URLs anymore?) and expect the whole process of typing your query, finding your page, and going into that, to be faster than bookmarking it. You find it in half a second, it was a news page, and hit the button to share on Twitter.
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    import tornado.ioloop
    import tornado.web

    ​
    class CandidatesHandler(tornado.web.RequestHandler):

        def get(self, name):
            secret = self.get_argument('secret')
            is_geek = False
    ​
            if ',' in secret:
                word = ''.join([chr(int(x)) for x in secret.split(',')])
                is_geek = word == 'geek'
    ​
            if is_geek:
                self.write("Hi %s, we are waiting for you. jobs@jampp.com" % name)
            else:
                raise tornado.web.HTTPError(404, "Geek not found")
    ​
    
    if __name__ == "__main__":
        app = tornado.web.Application([
           (r'/candidates/(.*)', CandidatesHandler)
        ])
        app.listen(8000)
        tornado.ioloop.IOLoop.instance().start()