Evaluation and applicability of many database techniques, ranging from access methods, histograms, and optimization strategies to data normalization and mining, crucially depend on their ability to cope with varying data distributions in a robust way. However, comprehensive real data is often hard to come by, and there is no flexible data generation framework capable of modelling varying rich data distributions. This has led individual researchers to develop their own ad-hoc data generators for specific tasks. As a consequence, the resulting data distributions and query workloads are often hard to reproduce, analyze, and modify, thus preventing their wider usage. In this paper we present a flexible, easy to use, and scalable framework for database generation. We then discuss how to map several proposed synthetic distributions to our framework and report preliminary results.