Dealing with the abundance of event data is one of the main process discovery challenges. Current process discovery techniques are able to efficiently handle imported event log files that fit in the computer’s memory. Once data files get bigger, scalability quickly drops since the speed required to access the data becomes a limiting factor. This paper proposes a new technique based on relational database technology as a solution for scalable process discovery. A relational database is used both for storing event data (i.e. we move the location of the data) and for pre-processing the event data (i.e. we move some computations from analysis-time to insertion-time). To this end, we first introduce DB-XES as a database schema which resembles the standard XES structure, we provide a transparent way to access event data stored in DB-XES, and we show how this greatly improves on the memory requirements of the state-of-the-art process discovery techniques. Secondly, we show how to move the computation of intermediate data structures to the database engine, to reduce the time required during process discovery. The work presented in this paper is implemented in ProM tool, and a range of experiments demonstrates the feasibility of our approach.