  The marriage of wireless big data and machine learning techniques
revolutionizes the wireless system by the data-driven philosophy. However, the
ever exploding data volume and model complexity will limit centralized
solutions to learn and respond within a reasonable time. Therefore, scalability
becomes a critical issue to be solved. In this article, we aim to provide a
systematic discussion on the building blocks of scalable data-driven wireless
networks. On one hand, we discuss the forward-looking architecture and
computing framework of scalable data-driven systems from a global perspective.
On the other hand, we discuss the learning algorithms and model training
strategies performed at each individual node from a local perspective. We also
highlight several promising research directions in the context of scalable
data-driven wireless communications to inspire future research.
