Graph Neural Networks are popular these days due to their strong capability in modeling and predicting complex graph-structured data. However, real-world graphs are usually giant in nature, containing millions of nodes and billions of edges. To enhance the training scalability, many mini-batch GNN training methods have been proposed recently. However, we observe two significant gaps that hinder the efficient and scalable training of GNNs. In this chapter, we first provide a structured review of these existing works and categorize them based on their position in the GNN training stack, namely the graph sampling algorithm and the GNN training system. Then, we analyze the shortcomings of existing work and propose our solutions, namely a scalable graph sampling algorithm called feature-oriented sampling and an efficient GNN training system called DUCATI to address them.