Generating synthetic data is useful in multiple application areas (e.g., database testing, software testing). Nevertheless, existing synthetic data generators generally lack the necessary mechanism to produce realistic data, unless a complex set of inputs are given from the user, such as the characteristics of the desired data. An automated and efficient technique is needed for generating realistic data. In this paper, we propose ReX, a novel extrapolation system targeting relational databases that aims to produce a representative extrapolated database given an original one and a natural scaling rate. Furthermore, we evaluate our system in comparison with an existing realistic scaling method, UpSizeR, by measuring the representativeness of the extrapolated database to the original one, the accuracy for approximate query answering, the database size, and their performance. Results show that our solution significantly outperforms the compared method for all considered dimensions.