In many research projects the analysis of produced data plays a central role. This may involve a large number of software tools, reference data and pipelines used to elaborate the results. While the process sounds trivial, reproducibility is often a burden as many pieces of the puzzle may be missing a few months later or on the computer of another researcher. We will discuss one available technology, namely docker containers, to ensure systematic reproducibility in data science. The audience will get to use the technology on a cloud computing server giving them the opportunity to experience its advantages first hand.