This paper presents a hybrid parallel processing system, named Parallel Secondo. It combines the Hadoop framework and a set of single-computer Secondo databases, in order to introduce the mobility data procedures into the parallel processing community, and vice versa. The system keeps the front-end and the executable language of Secondo to allow the users to state their parallel queries like common sequential queries. Besides, a set of auxiliary scripts is provided so as to make it easier to manage the system no matter how large the underlying cluster is, and keep the Hadoop platform as a transparent level of the system. Further, a parallel data model is also proposed in this paper to encapsulate all available Secondo data types and operators. Thereby, it is able to transform any Secondo sequential query to its corresponding parallel expression. For instance, all example queries in the moving objects database benchmark BerlinMOD are transformed, and two of them are demonstrated in this paper. In the last evaluations, this paper illustrates that Parallel Secondo is not only a practical but also an efficient system. For queries involving large amounts of data, it performs both linear speed-up and scale-up.