Learn how the OmniSci GPU-Accelerated SQL engine fits into the overall RAPIDS partner ecosystem for open source GPU analytics. Using open data, we'll show how to ingest data that's from both streaming and standing sources, perform descriptive statistics and feature engineering using SQL and cuDF, and return the results as a GPU DataFrame. We'll also describe how data science workflow can be accomplished using tools from the RAPIDS ecosystem, all without the data ever leaving the GPU.
According to a major car manufacturer, modern vehicles are collecting and sharing more than 25 gigabytes of data per hour, from dozens of sensors focused inside and outside the car. Compound that rate of collection across the growing fleets of connected vehicles, and the automotive industry is facing a stiff new challenge: making hundreds of billions of location-intelligent data points comprehensible, actionable, and predictive. GPUs running OmniSci's extreme analytics platform are uniquely capable of solving this problem, with orders-of-magnitude faster SQL queries, and full-fidelity rendering on the GPU. In this talk, Aaron Williams will use a real-world example to share best practices for analyzing a large dataset of driving behavior, to lower risk and cultivate better drivers.