Case Study: TomTom achieves faster time-to-market and scalable ML-driven navigation with Databricks

TomTom - Customer Case Study

TomTom, a leader in navigation and automotive technology, struggled to scale IT and data science operations to handle massive sensor streams from 600 million devices (over 80 billion geo-coordinates daily, ~150 trillion data points). The volume and velocity of data made advanced analytics and autonomous-driving research slow to deliver, while data engineers spent too much time managing clusters and infrastructure instead of feeding models and insights to product teams.

By adopting Databricks’ unified analytics platform—managed clusters, collaborative notebooks (SQL/Scala/Python/R), MLflow for experiment tracking, and Delta Lake for ACID data—TomTom simplified infrastructure and sped up ML workflows. The result: improved operational efficiency, better cross-team collaboration, and a faster time-to-market for new features, shrinking prototype-to-production timelines from weeks to days.

TomTom

Sergio Ballasteros

Data Scientist