The Model-Driven Engineering (MDE) paradigm has been successfully embraced for manufacturing maintainable software in several domains while decreasing costs and efforts. One of its principal concepts is rule-based Model Transformation (MT) that enables an automated processing of models for different intentions. The user-friendly syntax of MT languages is designed for allowing users to specify and execute these operations in an effortless manner. Existing MT engines, however, are incapable of accomplishing transformation operations in an acceptable time while facing complex transformations. Worse, against large amount of data, these tools crash throwing an out of memory exception. In this paper, we introduce ATL-MR, a tool to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We briefly present an overview of our approach, we describe the changes with respect to the standard ATL transformation engine, finally , we experimentally show the scalability of this solution.