In recent years, the amount of data highly increases through cheap hardware, fast network technology, and the increasing digitization within most domains. The data produced is oftentimes heterogeneous, dynamic and originates from many highly distributed data sources. Deriving information and, as a consequence, knowledge from this data can lead to a higher effectiveness for problem solving and thus higher profits for companies. However, this is a great challenge – oftentimes referred to as Big Data problem. The data mashup tool FlexMash, developed at the University of Stuttgart, tackles this challenge by offering a means for integration and processing of heterogeneous, dynamic data sources. By doing so, FlexMash focuses on (i) an easy means to model data integration and processing scenarios by domain-experts based on the Pipes and Filters pattern, (ii) a flexible execution based on the user’s non-functional requirements, and (iii) high extensibility to enable a generic approach. A first version of this tool was presented during the ICWE Rapid Mashup Challenge 2015. In this article, we present the new version FlexMash 2.0, which introduces new features such as cloud-based execution and human interaction during runtime. These concepts have been presented during the ICWE Rapid Mashup Challenge 2016.