This contribution discusses interactions between kernel methods, frame analysis, and persistent homology. To this end, we explain recent connections between these research areas, where special emphasis is placed on the discussion of reproducing kernel Hilbert spaces and persistent mechanisms. We show how interactions between these novel methodologies give new opportunities for the construction of numerical algorithms to analyze properties of data that are so far unexplored.