4 posts found under,


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

Back when we started the current incarnation of the MapD Core database, we wrote our own parser (written using flex and GNU bison), semantic analysis and optimizer. This approach offers the most control since everything in the pipeline can be adjusted to the unique needs of our system, but we've realized that our main strength lies in the actual execution of the queries. In the context of the limited resources of a startup, we have to pick our battles. We soon faced a dilemma... read more

Continuing where we left off in our earlier post on MapD 2.0’s Immerse visualization client, today we want to walk you through some of version 2.0’s major improvements to our GPU-accelerated Core database and Iris Rendering Engine. Before we delve into the details, main themes for this release are: speed, robustness, and further visual analytics power. Our system is able to steadily deliver extremely fast query speeds across a larger set of SQL queries and when analyzing dat... read more

The taxi dataset is one of the most popular on our site and for good reason, it is not often that you can get behind the wheel of a supercomputer for free. Still, without direction, it can be hard to uncover the insights in the data that often give our audiences a rush. With that in mind we will be creating a series of these “cheatsheets” to help you grasp the power of speed at scale. Each post will talk about how to interact with the GPU-powered relational database (MapD... read more