Visualization is a highly data intensive science: visualization algorithms take as input vast amounts of data produced by simulations or experiments, and then transform that data into imagery. It turns out, as we shall explore in this chapter, that visualization reveals a somewhat different view of scientific data management challenges than are examined elsewhere in this book. For example, a data ordering and storage layout that works well for saving data from memory to disk may not be the best thing for subsequent visual data analysis algorithms. This chapter will present four broad topic areas under this general rubric: (1) a view of SDM-related issues from the perspective of implementing a production-quality, parallel capable visual data analysis infrastructure; (2) novel data storage formats for multi-resolution, streaming data movement, access and use by post-processing tools; (3) data models, formats and APIs for performing efficient I/O for both simulations and post-processing tools, discussion of issues and previous work in this space; (4) how combining state-of- the-art techniques from scientific data management and visualization enables visual data analysis of truly massive datasets.