A data transform leverages a known hierarchy within a target data structure, in order to improve query and mapping capabilities and enhance performance. Where a target data structure is hierarchical, output data of that target data structure is often built in the document order of the nodes in the structure (from top down and from left to right). Hence, when the data for a child node in the target structure is being built, the data for the parent nodes of the child node has been built. Embodiments utilize this available portion of the target data in the form of target columns, to increase processing efficiency of the transformation process. Use of target columns according to embodiments may also allow powerful and concise expression of mapping logic in the transform, facilitating the use of functions such as selection (e.g. Where clauses), uniqueness (e.g. DISTINCT), ordering (Order By, Group By), and Aggregation.