Using a single type of database solution to support real-world applications is becoming more and more challenging because of the volume and variety of data. For instance, the data collected for the transportation industry include both structured and unstructured data. Using solely a single type of database solution---relational database system-only or graph database-only---to store and manage data can be challenging. As real-world applications ask even more complex questions related to data, the database solution should be able to facilitate answering these questions in a reasonable time. Hence, for my MSc thesis, I present a hybrid database solution to support transportation analytics. The hybrid solution consists of relational and non-relational databases, pooling their strengths to support the demands of the modern application. I also demonstrate this as a practical solution with two case studies on improving emergency medical services (EMS) response times by having the support of the presented hybrid database solution. My key contributions to this thesis include (a) the design and implementation of the hybrid database for supporting transportation analytics, (b) algorithms to extract and convert connected linear shapefiles into a graph network, and (c) two case studies on improving EMS response times.