Technical Deep-Dive

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!

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