Most vehicle tracking systems ask one database to do everything. For example, store the road network, query it with recursive CTEs, write live positions to the same database, run analytics on the same tables, and so on. It works until the graph queries slow down, high-frequency writes start competing with reads, and analytics queries time out.This article shows a different approach: three databases, each doing what it's genuinely good at. Neo4j Aura for the road network graph, Databricks Lakebase for live vehicle positions, and Databricks Lakehouse for historical analytics. Ten simulated vehicles move around a real city following real road connections loaded from OpenStreetMap. Two Streamlit dashboards show live positions and analytics. The whole system is driven by a single YAML configuration file, so switching from London to San Francisco or Singapore means changing a single file and rerunning five notebooks.