Analytics on Big Data is critical to derive business insights and drive innovation in today's Internet companies. Such analytics involve complex computations on large datasets, and are typically performed on MapReduce based frameworks such as Hive and Pig. However, in our experience, these systems are still quite limited in performing at scale. In particular, calculations that involve complex joins and aggregations, e.g. statistical calculations, scale poorly on these systems. In this paper we propose novel primitives for scaling such calculations. We propose a new data model for organizing datasets into calculation data units that are organized based on user-defined cost functions. We propose new operators that take advantage of these organized data units to significantly speed up joins and aggregations. Finally, we propose strategies for dividing the aggregation load uniformly across worker processes that are very effective in avoiding skews and reducing (or in some cases even removing) the associated overheads. We have implemented all our proposed primitives in a framework called Rubix, which has been in production at LinkedIn for nearly a year. Rubix powers several applications and processes TBs of data each day. We have seen remarkable improvements in speed and cost of complex calculations due to these primitives.