This paper addresses the efficient generation of models for cyber-physical systems from large historical data sets. A cyber-physical system (CPS) is a system composed of physical subsystems together with computing and networking. CPS models are required for monitoring and control of the physical processes. Such models are in general hybrid models that take into account both discrete control signals and continuous system behaviour. Model-learning is the key to a new generation of intelligent automation systems: Automatic generation of models from system observations allows to model complex CPS in cases where manual model creation is time-consuming, expensive or not even possible. In general, the quality of the generated models increases with the size of training data. However, model learning from large historical data sets is in many cases time-consuming. For this reason, MapReduce algorithms are proposed in the present work that allow for efficient model learning.