We present a cloudbased technology, that allows “big geodata analysis” in process chains, that make use of actinia and GRASS GIS. Geo- and EO-data are more and more available, especially the growing Open Data policy of organisations fuels this process. With this awareness and demand for geo-based decision support increases. We want to present an implementation of the new paradigm of “bring processes to the data”. In the talk we present how big geodata analysis process chains could be easily implemented in a cloud environment by using GRASS GIS and actinia (OSGeo project & OSGeo Community project, respectively) for the process algorithms. The combination of these allows cloud optimized geodata processing triggered through simple API-calls. In the first part we show the architectural design and the interactions between actinia and GRASS GIS. In the 2nd part we present examples of process chains. These are a) automated surface-type detection from orthophotos, b) on-demand creation of cloud free Sentinel-2 scenes using temporal interpolation, and lastly c) gap-filling of high-resolution land surface temperature data from MODIS-satellite data.