In the context of increasing urban populations and rising temperatures, mass transit systems are vital for reducing congestion and pollution, thereby enhancing urban livability. Real-time analytics are essential for optimizing these systems, offering insights into usage patterns, revenue, and service effectiveness, which can help authorities in schedule adaptation, route efficiency assessment, and fare adjustments. IoT devices complement this by enabling functions like predictive maintenance and passenger communication, although continuous data availability is crucial for safety-critical operations. Apache Druid is highlighted as a scalable and high-performance solution for mass transit analytics, capable of handling large volumes of real-time data efficiently. A case study of an analytics provider for over 100 public transit networks illustrates the benefits of switching from PostgreSQL to Druid, noting significant cost reductions and improved scalability and reliability. The transition also supports future-proofing by facilitating a move from batch to streaming data processing using Amazon Kinesis, enhancing both data consistency and reliability.