Summarization is an important means to cope with the challenges of big data. Summaries can help to achieve a first overview, they can be used to characterize subsets, they allow for the targeted access to data, and they build the basis for visualization techniques. In the present article, we point out the role of summaries as well as potential application scenarios. As examples, summarization techniques for spatial data (as an example for specific low dimensional techniques) and for general metric spaces (as a generic example with a broad spectrum of applications) are described. Furthermore, their use for resource selection and resource visualization in large distributed scenarios is outlined.