While 2016 was the year of the GPU for a number of reasons, the truth of the matter is that outside of some core disciplines (deep learning, virtual reality, autonomous vehicles) the reasons why you would use GPUs for general purpose computing applications remain somewhat unclear.
2016 was a pretty amazing year for MapD. Not only did we launch our company with the announcement of our A Round of funding in late March, but we were able to steadily build on that event throughout the year, culminating in the release of our 2.0 version of the product just nine months later.
After many months of hard work, refinement and improvement, we’re very happy to announce the release of version 2.0 of the MapD Core database and Immerse visual analytics platform.
A couple of months back we hosted a BrightTALK webinar with Sam Madden, MIT Professor and Chief Scientist at Cambridge Mobile Telematics.
This morning Google Cloud announced the upcoming availability of powerful, innovative GPU instances. As a an beta tester of the new offering we had the opportunity to take the instances for a spin and test them out against the 1.2 billion row taxi dataset.
A common question faced in the petabyte economy is when, and how, to embrace a distributed, scale out architecture. I will argue here that it makes sense to push for the simplest and cheapest solution that will solve the problem.
What is obscured in the vitriol, the accusations and the gaffes, however, is that money still fuels the American political process. Despite the emergence of a billionaire candidate, this cycle is no different - the money is as prevalent as ever.
The Finovate show one of the best, most dangerous shows in any industry and is the crown jewel of the FinTech event calendar. Its one of the best because of its rigorous selection process coupled with a hyper-engaged, highly informed audience.
Today we are pleased to announce that In-Q-Tel, the non-profit strategic investor that identifies innovative technology for the U.S. Intelligence Community, participated in our previously announced Series A round.
With the latest addition to our public demos, we have the absolutely spectacular 1.2 billion row taxi/limo/uber/lyft dataset from NYC. The dataset is comprised of staggering detail (full GPS, transaction type, passenger counts, timestamps) from January 2009 through June 2015 (essentially the birth of rideshare).