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.

Today I’m proud to announce that MapD Technologies has secured $25M in funding in a Series B round lead by New Enterprise Associates (NEA) with participation from NVIDIA, Vanedge Capital, and Verizon Ventures.

Organizations are visualizing and exploring data in ways we once only associated with science fiction films.

We felt it wasn’t fair that only features in our major releases were getting the limelight, so this will be the first in a series of short blog posts featuring an interesting feature or improvement in our regular minor releases of MapD’s GPU-accelerated Core database and Immerse visualization software.