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Unlike traditional object stores, Augmented Reality (AR) query workloads possess several unique characteristics, such as spatial and visual information. Such workloads are often keyed on a variety of attributes simultaneously, such as device orientation and position, the scene in view, and spatial anchors. The natural mode of user-interaction in these devices triggers queries implicitly based on the field in the user's view at any instant, generating data queries in excess of the device frame rate. Ensuring a smooth user experience in such a scenario requires a systemic solution exploiting the unique characteristics of the AR workloads. For exploration in such contexts, we are presented with a view-maintenance or cache-prefetching problem; how do we download the smallest subset from the server to the mixed reality device such that latency and device space constraints are met? We present a novel data platform - DreamStore, that considers AR queries as first-class queries, and view-maintenance and large-scale analytics infrastructure around this design choice. Through performance experiments on large-scale and query-intensive AR workloads on DreamStore, we show the advantages and the capabilities of our proposed platform.