From the viewpoint of a solid-state chemist, topology is a key feature of crystalline materials, as it influences many of their physical and chemical properties, including electrical and optical properties, magnetism, thermal and mechanical behavior, etc. Crystalline topology has long been used to rationalize different categories of behavior, and researchers often look for materials with specific topologies in order to achieve desired properties. So far, this has been a mostly manual process: despite the growing number (and size) of available databases of crystalline materials, these structures are not annotated with topology information. Here, we have shown that it is now possible to achieve this goal at very large scale, by combining bonding detection algorithms (if bond information is not already present in the structures) and recent algorithmic advances in topology identification through the CrystalNets.jl library.