Recently in Jampp, I had the chance to switch some of our data science environment from Python to Julia. For various reasons, its type system is, in my opinion, one of the best language features. The most obvious one is the performance enhancements it allows. I will not, however, address that point here: it has been benchmarked very well in several places. Instead, I will briefly show a safety advantage this type system brings that is really handy for data science.
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.
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.
Jampp is a mobile app marketing company. Our platform helps mobile app advertisers to acquire and re-engage their users globally. This enables brands to go beyond simple installs and re-targeting clicks, as it optimizes for in-app activity and conversions, thus maximizing lifetime value. Our intention in this initial post is to give a brief overview of the different systems that power our platform. In future posts we will delve deeper into most of the topics covered here and others.