Today, the amount of data highly increases within all domains due to cheap hardware, fast network connections, and an increasing digitization. Deriving information and, as a consequence, knowledge from this huge amount of data is a complex task. Data sources are oftentimes very heterogeneous, dynamic, and distributed. This makes it difficult to extract, transform, process and integrate data, which is necessary to gain this knowledge. Furthermore, extracting knowledge oftentimes requires technical experts with the necessary skills to conduct the required techniques. For my PhD thesis, I am working on a new and improved approach for data extraction, processing, and integration by: (i) facilitating the definition and processing of data processing and integration scenarios through graphical creation of flow models, (ii) enabling an ad-hoc, iterative and explorative approach to receive high-quality results, and (iii) a flexible execution of the data processing tailor-made for users’ non-functional requirements. By providing these means, I enable a more flexible data processing by a wider range of users, not only limited to technical experts. This paper describes the approach of the thesis as well as the publications until today.