While scientific inquiry crucially relies on the extraction of patterns from data, we still have a very imperfect understanding of the metaphysics of patterns-and, in particular, of what it is that makes a pattern real. In this paper we derive a criterion of real-patternhood from the notion of conditional Kolmogorov complexity. The resulting account belongs in the philosophical tradition, initiated by Dennett (1991), that links real-patternhood to data compressibility, but is simpler and formally more perspicuous than other proposals defended heretofore in the literature. It also successfully enforces a non-redundancy principle, suggested by Ladyman and Ross (2007), that aims at excluding as real those patterns that can be ignored without loss of information about the target dataset, and which their own account fails to enforce.