Format transformation is one of the most labor intensive tasks of a data wrangling process. Recent advances in programming by example proposed synthesis algorithms that showed promising results on spreadsheet data. However, when employed on repositories consisting of multiple sources and large number of examples, such algorithms manifest scalability issues. This paper introduces a new transformation synthesis technique based on edit operations that enables efficient learning of transformation programs. Empirical results show comparable effectiveness and dramatic improvements in efficiency over the state-of-the art.