In today's data-rich world, pattern mining techniques allow us to extract knowledge from data. However, such knowledge can take many forms and often depends on the application at hand. This calls for generic techniques that can be used in a wide range of settings. In recent years, constraint programming has been shown to offer a generic methodology that fits many pattern mining settings, including novel ones. Existing constraint programming solvers do not scale very well though. In this talk, I will review different ways in which this limitation has been overcome. Often, this is through principled integration of techniques and data structures from pattern mining into the constraint solvers.