Case Study: DNV GL achieves 100x faster data processing and scalable ML with Databricks

DNV GL - Customer Case Study

DNV GL is a global technical assurance and advisory firm serving the energy sector that combines smart-meter, climatological and socio-economic data to build predictive models. One of its analytics frameworks hit a scalability ceiling as data volumes and model complexity grew: a single-server legacy system caused four- to five-day time-to-insight (vs. 24-hour SLAs), frequent costly platform failures, and heavy DevOps overhead the team wasn’t staffed to support.

After a successful POC they moved to Databricks’ managed Spark platform on AWS, which let them provision clusters in minutes and run machine learning at scale. Processing sped up by nearly 100x (one job fell from 36 hours to ~23 minutes), DevOps time and costs were eliminated, security and role-based access were maintained, and DNV GL can now deliver advanced analytics and products to clients faster and more cost-effectively.

DNV GL

Jonathan Farland

Senior Data Scientist