We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly approximated using statistical or linear programming techniques. This enables new applications such as exploratory data analysis for feature engineering and other fields of data science. Moreover, it removes the need to compromise when building a data cube—all columns we might ever wish to use can be included as dimensions. Our approach also speeds up certain dice, roll-up, and drill-down operations on data cubes with hierarchical dimensions compared to traditional data cubes.