Our latest demo of MapD Core and MapD Immerse reveals the vast scope of marine activity around America’s shores–everything from the tracks of commercial freighters to the patrols of military vessels to the lazy patterns of pleasure boats out for a Sunday sail on San Francisco Bay.

Today, with great pride, MapD is announcing a partnership with the Harvard Center for Geographic Analysis (CGA).

We’re excited to announce our newest addition to the MapD executive team. Aaron Williams joins us today as VP of Global Community, responsible for fostering our growing developer, user and open source communities.

This week we release version 3.1 of MapD, which comes after some truly giant news over the last few weeks, and adds a number of useful new features.

One of the things we are most excited about as a newly open source company is the potential to help kickstart a larger ecosystem of GPU computing. This is why we are particularly excited about our work with Continuum Analytics and H2O.ai to found the GPU Open Analytics Initiative (GOAI) and its first project, the GPU Data Frame (GDF), as our first step toward an open ecosystem of end-to-end GPU computing.

Since starting work on MapD more than five years ago while taking a database course at MIT, I had always dreamed of making the project open source. It is thus with great pleasure to announce that today our company is open sourcing the MapD Core database and associated visualization libraries, effective immediately.

We’re very happy to announce that with today’s release of version 3.0 of the MapD Analytics Platform we're bringing GPU-accelerated analytics onto distributed clusters!

The MapD Immerse visual analytics client has a core feature we refer to as crossfilter, which allows a filter applied to one chart to simultaneously be applied to the rest of the charts on a dashboard.