Temporal Graph Neural Networks (T-GNNs) have become the de facto solution for representation learning on dynamic graphs, enabling state-of-the-art performance on tasks such as temporal link prediction and recommendation. However, existing T-GNN training pipelines suffer from scalability issues due to ill-suited batching and high input data loading costs, which severely limit their efficiency on large-scale graphs. This chapter addresses both these bottlenecks with two complementary system prototypes. First, we propose ETC, a generic framework that introduces a theoretically grounded batch splitting algorithm and a three-step deduplication policy to improve computation throughput and reduce I/O overhead. Second, we present SIMPLE, a dynamic data placement system that maintains a GPU buffer for frequently accessed inputs, optimizing data reuse through an interval selection algorithm with approximation guarantees. Together, ETC and SIMPLE significantly accelerate T-GNN training, achieving up to 62.4 \(\times \) speedup over state-of-the-art baselines while preserving model accuracy, as demonstrated by extensive experiments on real-world datasets.