To genuinely understand reservoir behavior, the oil and gas industry needs tools that can track and analyze data over long periods of time and for many unique variables. This post shows examples of tracking reservoir behavior across time. Traditional BI and GIS tools are too restrictive but with OmniSci's GPU and CPU innovations, accelerated analytics on billio3ns of rows of data becomes possible.
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.
MapD was built from the ground up to enable fully interactive querying and visualization on multi-billion row datasets. An important feature of our system is the ability to visualize large results sets, regardless of their cardinality
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 onto SQL result sets.
Using Random Forest and LSTM, Abraham Duplaa demonstrates how OmniSciDB and OmniSci Immerse complements the PyData ecosystem of data science tools.
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 stars. Using OmniSci Immerse, we walk through visualizing and examining this dataset, 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.