The field of relational data modeling and generation is witnessing significant advancements, driven by the development of innovative architectures and techniques. A key direction of research is the creation of more accurate and efficient models for relational data, including graph-based approaches that can capture complex structural patterns and long-range dependencies. Another area of focus is the generation of synthetic relational data, which is essential for privacy-enhancing technologies and data augmentation. Noteworthy papers in this area include the introduction of the Relational Graph Transformer, which achieves state-of-the-art performance on relational data tasks, and the Graph-Conditional Relational Diffusion Model, which jointly models all tables in a relational database without imposing any order. Additionally, the WikiDBGraph dataset provides a large-scale graph of interconnected tabular databases, enabling collaborative learning and foundation model training. These advances have the potential to significantly improve the accuracy and efficiency of relational data modeling and generation, with applications in various fields, including social science research and data science.