Both in theory and practice, coresets provide the state of the art solution for problems such as k-means clustering, logistic regression or low rank approximation (SVD/PCA). This includes streaming and real-time "Big data" that may be distributed e.g. on a network ("cloud"). A coreset (or, core-set) for a given problem is a "compressed" representation of its input, in the sense that a solution for the problem with the (small) coreset as input would yield a provable (1+epsilon) factor approximation to the problem with the original (large) input. In this talk we will survey the main techniques for computing core-sets and see some applications for real-time drones navigation and weak "Internet of Things" devices.