Databricks helps TKE simplify complex engineering problems, allowing teams to focus on delivering predictive insights that improve elevator servicing. TKE, a global provider of elevators and vertical transportation systems, aimed to use data and AI for predictive maintenance and AI-assisted diagnostics while offering equipment insights through a digital portal. Fragmented systems and limited IoT data access hindered scaling. TKE adopted the Databricks Platform to unify and process more than 500M daily events across global operations. The company collects more than 500 million telemetry events per day from connected elevators to monitor health and predict service needs, shifting from reactive fixes to proactive maintenance. TKE analyzes data to detect abnormal patterns and early signs of component wear before service disruptions. It also modernized its Digital Operations Center by turning insights from MAX, its IoT and predictive maintenance platform, into actionable guidance for technicians, such as “inspect the door on the 5th floor” or “check sensor alignment.” This reduces repeated service calls and helps technicians resolve issues efficiently. TKE also delivers customer value through a self-service digital portal with operational status, service history, and equipment information. Managing IoT data at this scale poses challenges, as telemetry signals like door cycles and error data are generated continuously, and traditional relational databases were not designed for this volume and velocity. TKE needed to integrate telemetry with fragmented enterprise systems and unstructured sources like manuals, service records, and contracts.