Technical Deep-Dive
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.
The newest release of "pymapd (0.7)" picks up on the work from 2018; here’s what changed since the last pymapd release and our plans for pymapd and related tools in the first half of 2019.
The Gaia space observatory has measured 1.7 billion stellar positions. Using OmniSci Immerse, we create an interactive map of the galaxy to visualize this star data at various scales, to draw insights about stars and the Milky Way.
A few years back, the American Statistical Association put out a dataset of hundreds of millions of US airline flights from 1987 to 2008, as part of a supercomputing competition. The dataset includes every single flight record known by Bureau of Transportation Statistics for that two decade period; every prop plane, every jet plane, balloon or blimp.
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.
The latest release of OmniSci Cloud includes a much sought after feature - access to OmniSci's Apache Thrift API
Take a spin around the virtual racetrack in the OmniSci Grand Prix TA at GTC 2019.
Learn how Verizon leveraged MapD’s SQL engine and visual analytics platform to provide industry-leading network reliability.
I recently came across Big Data Ball, an NBA stats distributor. They offered a dataset called: “NBA Play-By-Play Stats – 2004 to 2017”. It includes all events that occur in a game including: active lineups, shot distances, shot locations in X, Y coordinates, assists, time remaining, and tons of other interesting data points. Game on!
