According to data compiled by the National Highway Traffic Safety Administration, in 2016, an average of ~100 people were killed in automobile accidents every day in the United States. Agero, a market leader in software-enabled driver assistance services, has responded to this growing problem with a breakthrough consumer app that provides near real-time driver behavior analysis and actionable insights to its users on how to become safer drivers. As part of this effort, we have developed a methodology to identify the most frequent routes that each driver travels by applying Dynamic Time Warping time-series analysis techniques to spatial data. In this talk, we will give a high-level overview of the methodology, and discuss the performance improvement achieved by transitioning the software from stand-alone Python into PySpark + Databricks. Discussion points will include how to determine the best way to (re)design Python functions to run in Spark, the development and use of user-defined functions in PySpark, how to integrate Spark data frames and functions into Python code, and how to use PySpark to perform ETL from AWS on very large datasets.