Technical Deep-Dive

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!

With the help of Apache Arrow, an efficient data interchange is created between MapD, pygdf, and machine learning hardware acceleration tools such as h2o.ai, PyTorch, and others.

Explore and visualize Bitcoin transaction data with MapD.

At MapD, we've long been big fans of the PyData stack, and are constantly working on ways for our open source GPU-accelerated analytic SQL engine to play nicely with the terrific tools in the most popular stack that supports open data science.

MapD now lets you explore LiDAR data in 3D, unlatching its true potential

As companies perform more real-time analytics, the Extract-Transform-Load (ETL) data processing model becomes too slow to support the business. Here’s how to run an Extract-Load-Transform (ELT) pipeline with OmniSciDB.

Tips to apply MapD 4.1 features on geospatial data

The United States Department of Transportation (USDOT) along with many partners from the industry and academia are researching to evaluate the ability of connected vehicles to generate and communicate different types of messages using cellular and dedicated short range communications (DSRC) infrastructure.

Open source Extreme analytics on an open source container platform.