Relational databases are the backbone of modern data infrastructure, supporting much of the digital economy. Despite their importance, their rich relational information is often overlooked. Most predictive pipelines, in fact, still flatten relational schemas into a single table, discarding higher-order relational structure, cross-table dependencies, and forcing reliance on costly and fragile feature engineering, sensitive to expert skill. This thesis embraces a graph native learning alternative: we cast relational schemas into heterogeneous temporal graphs. Each table in the relational schema becomes a node type, rows become nodes and foreign keys become typed edges. Some node types are associated with time attributes, representing the timestamp at which a node appears.