With the advances in location-acquisition techniques, such as GPS-embedded phones, enormous volume of trajectory data is generated, by people, vehicles, and animals. These trajectory data is one of the most important data sources in many urban computing applications, e.g., the traffic modeling, the user profiling analysis, the air quality inference, and the resource allocation. To utilize the large scale trajectory data efficiently and effectively, cloud computing platforms, e.g., Microsoft Azure, are the most convenient and economic way. However, the traditional cloud computing platforms are not designed to deal with the spatiotemporal data, such as trajectories. To this end, we design and implement a holistic cloud-based trajectory data management system on Microsoft Azure to bridge the gap between the massive trajectory data and the urban applications. Our system can efficiently store, index and query massive trajectory data with three functions: 1) trajectory ID-temporal query, 2) trajectory spatiotemporal query, and 3) trajectory map-matching. The efficiency of the system is tested and tuned based on the real-time trajectory data feeds. The system is currently used in many internal urban applications, as we will illustrate as the case studies.