Today, organizations typically perform tedious and costly tasks to juggle their code and data across different data processing platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging because it requires quite good expertise for all the available data processing platforms. In this report, we present Rheem, a general-purpose cross-platform data processing system that alleviates users from the pain of finding the most efficient data processing platform for a given task. It also splits a task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). To offer cross-platform functionality, it features (i) a robust interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Rheem is released under an open source license.