Scalability is an issue in visualization and visual analytics. The dataset sizes we can handle are lagging behind by several levels of magnitude compared to domains such as database, artificial intelligence, and simulation. The standard method for addressing scalability consists of adding more resources: more processors, more GPUs, more memory, and faster networks. Unfortunately, this method will not solve the visualization scalability problem alone. It does not solve the crucial issues of maintaining latency under critical limits to allow exploration and taming human attention during long-lasting computations. Progressive Data Analysis (PDA) emerged about a decade ago to address this scalability problem, showing promising but challenging solutions. I will show a few examples of applications. However, PDA is still lagging behind, mainly because of domain boundaries coming from academic research.