5 posts found under,

MapD

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. The code is available on Github under an Apache 2.0 license. It has everything you need to build a fully functional installation of the MapD Core database, enabl... read more

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. This provides a natural interface for data exploration, allowing a multi-dimensional view of data even as a user drills deep into a dataset. From a technical perspective, crossfiltering is not difficult (on the surface). Behind each Immerse chart is a SQL statement. When an e... read more

While we love datasets of all shapes and sizes at MapD, Twitter holds a special place in our hearts. This is perhaps because we find Twitter data to be almost peerless among public datasets in its ability to provide a glimpse into the human experience - revealing what people are saying when and where. Twitter is powerful in that it provides insight into a wide variety of social phenomena both at the level of individual tweets as well as rolled up by user, geography or topic/... read more

Note: The following post was originally published on June 23, 2015 on Nvidia's Parallel ForAll blog. This is only an excerpt, you can find the full original here. Co-written by Alex Suhan and Todd Mostak of MapD. At MapD our goal is to build the world’s fastest big data analytics and visualization platform that enables lag-free interactive exploration of multi-billion row datasets. MapD supports standard SQL queries as well as a visualization API that maps OpenGL primitives... read more

MapD is a next-generation data analytics platform designed to process billions of records in milliseconds using GPUs. It features a relational database backend with advanced visualization and analytic features to enable hyper-interactive exploration of large datasets. The inspiration for MapD came from our own struggles wrestling with big data, marked by frustrating encounters with tempermental clusters and overnight queries. Such "data trauma" is rampant. Even though our... read more