Developing new structure-property relationships or machine learning regression methods requires large self-consistent datasets that are rare in literature. We use and develop modern workflow tools like fireworks and luigi to organize, distribute, and analyze many thousands of DFT calculations. These methods drive down the cost of investigating new chemistries and materials while simultaneously making the results accessible and searchable. This approach also allow for on-line material optimization or active learning approaches to be applied and tested quickly.