Jonathan Farland, Senior Data Scientist, DNV GL and Kyle Pistor, Solutions Engineer, Databricks
Smart meter sensor data presents tremendous opportunities for the energy industry to better understand their customers and anticipate their needs. With smart meter data, energy industry data analysts and utilities are able to use hourly readouts to gain high resolution insights into energy consumption patterns across structures and customer types, and in addition gain near real time insights into grid operations.
Join Jonathan Farland, a technical consultant at DNV GL Energy, to learn how this globally renowned energy company is processing data at scale and mining deeper insights by leveraging statistical learning techniques. In this talk, Jon will share how DNV GL is using Apache Spark and Databricks to turn smart meter data into insights to better serve their customers by:
- Accelerating data processing compared to competing platforms, at times by nearly 100 times faster without incurring additional operational costs.
- Scaling to any size on-demand while being able to decouple compute and storage resources to minimize operational expense.
- Eliminating the need to spend time on DevOps, allowing their data scientists and engineers to focus on solving data problems.