1410 Rue Stanley
Suite 606
Montreal, QC H3A 1P8

122 East 42nd Street
Suite 2005
NY, NY 10168




1410 rue Stanley 606
Montréal, QC H3A 1P8

122 East 42nd Street
Suite 2005
NY, NY 10168




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Sunil Rottoo's picture

Computational Complexity, Cloud Computing and Big Data

War Story

Most of the data we use here at Datacratic is stored on Amazon’s S3 with the compute jobs running on EC2 instances accessing this data as needed. A few months ago, our operations team noticed that our Amazon bills were unusually high. After analysis, it turned out that the problem was a parameter in a configuration file that was causing our code to perform a large number of list operations. In the process of looking into the cause of the problem we discovered a very interesting thing about the price of an API call.

Nicolas Kruchten's picture

Arbitraging an RTB Exchange

Last week, Bloomberg came out with an article on RTB arbitrage, which included a couple of sentences that made it sound a lot like it was possible to front-run an RTB auction: “Some buy from an exchange and sell it right back to that very same exchange” and “Some agencies are poorly connected to exchanges and can’t respond to a first auction in time, allowing middlemen to buy and flip within the same market”. This seemed surprising to me at first, given that all auction participants (as far as I know) get the same opportunity to bid on an impression, so how could you make money buying and selling the same impression on the same exchange? Upon further thought, however, here’s a theory about how it might work. A disclaimer up front, though: Datacratic is a software company and doesn’t engage in this practice nor has anyone ever asked us how to use our RTB Optimizer product to do this. What follows is just a bit of thinking out loud about the economics of the situation.

Nicolas Emiliani, RTB Technology Lead at Motrixi's picture

Tales of a RTBkit adventure.

RTB gave you the power so ... now what ?

Power comes with a price, in this case the price you pay looks like a pretty complex distributed system. Developing one of these systems will chew up resources, a lot of time, and if you are not experienced in such systems you will probably sink in dark waters. This is where Datacratic jumped in and opened up RTBkit (thanks, btw).

RTBkit will drastically reduce your implementation time, and by implementation time I mean the time it takes to be running a production DSP. RTBkit will solve a lot of your problems, but not all of them. So in this post you'll find what took us at Motrixi to get to that point.

Data-Driven Business Models: Challenges and Opportunities of Big Data

Recently, Datacratic's CTO, Jeremy Barnes was interviewed as an expert source for the Engaging Complexity: Challenges and Opportunities of Big Data report by Dr. Monica Bulger, Dr. Greg Taylor and Dr. Ralph Shroeder published by the Oxford Internet Institute and NEMODE.   This is an excellent resource that covers:

Visualizing High-Dimensional Data in the Browser with SVD, t-SNE and Three.js

Data visualization, by definition, involves making a two- or three-dimensional picture of data, so when the data being visualized inherently has many more dimensions than two or three, a big component of data visualization is dimensionality reduction. Dimensionality reduction is also often the first step in a big-data machine-learning pipeline, because most machine-learning algorithms suffer from the Curse of Dimensionality: more dimensions in the input means you need exponentially more training data to create a good model. Datacratic’s products operate on billions of data points (big data) in tens of thousands of dimensions (big problem), and in this post, we show off a proof of concept for interactively visualizing this kind of data in a browser, in 3D (of course, the images on the screen are two-dimensional but we use motion and perspective to evoke a third dimension). For the TL;DR crowd, here’s a link to a demo of what we came up with and the source code up on Github.