Businesses are drowning in data but starving for insight, making the hiring of a data science team vital. But what makes up a data science team? What are the best practices for data science workflows? And what do data scientists need to execute their data science workflow to the best of their ability?
We’ve listened to your feedback, and the result is an easier and faster Immerse SQL Editor with our recent 5.2 release. Find out how the updated platform allows you to run selected queries, incorporate query snippets and run previous SQL statements.
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 billions 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.