Scalability testing is a critical step in preparing a database system for deployment in production environments. However, developers are rarely granted access to full-scale real-world datasets due to privacy and security constraints. Instead, they must often rely on small, and sometimes redacted, samples. To conduct meaningful scalability evaluations, developers must interpret the semantics of the available data and generate large-scale synthetic datasets that faithfully preserve their statistical and structural properties. This paper proposes a tabular data augmentation method based on Generative Adversarial Networks (GANs), which enables the expansion of input tabular datasets to much larger scales while maintaining their original statistical characteristics and inter-table relationships. We have applied the proposed method to Japanese medical insurance claims data and demonstrate that the augmented datasets closely preserve the statistical distributions of the original records. Furthermore, we have conducted a scalability test using a commercial database engine, assessing both capacity utilization and query performance. Our findings suggest that GAN-based tabular data augmentation provides a viable path toward realistic and scalable database benchmarking in data-restricted environments.