The use of high-throughput density functional theory (DFT) calculations to screen for new materials and conduct fundamental research presents an exciting opportunity for materials science and materials innovation. High-throughput DFT typically involves computations on hundreds, thousands, or tens of thousands of compounds, and such a change of scale requires new calculation and data management methodologies. In this article, we describe aspects of the necessary data infrastructure for such projects to handle data generation and data analysis in a scalable way. We discuss the problem of accurately computing properties of compounds across diverse chemical spaces with a single exchange correlation functional, and demonstrate that errors in the generalized gradient approximation are highly dependent on chemical environment.