This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database genera- tor. RFF is a unique signature created in the emitter transmission chain by the hardware impairments. These impairments may be used as a secure identifier, as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification has mainly relied on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce RiFyFi, a virtual database generator and explore individually the impairment impact on the classification accuracy to highlight the most relevant impairment. Different transmission scenarios are then explored, such as the impact of the data type (being a preamble or a payload) and the data size. Design rules of real database are finally drawn for the different scenarios. We found out that the power amplifier imperfections play the biggest role in RFF accuracy and that average accuracy levels of 94% can be reached when combining the various hardware impairments at the transmitter.