In this talk we consider the problem of recovering a low-rank Tucker approximation to a massive tensor based solely on structured random compressive measurements (i.e., a sketch). Crucially, the proposed random measurement ensembles are both designed to be compactly represented (i.e., low-memory), and can also be efficiently computed in one-pass over the tensor. Thus, the proposed compressive sensing approach may be used to produce a low-rank factorization of a huge tensor that is too large to store in memory with a total memory footprint on the order of the much smaller desired low-rank factorization. In addition, the compressive sensing recovery algorithm itself (which takes the compressive measurements as input, and then outputs a low-rank factorization) can be designed to run in a time which principally depends only on the size of the sought factorization, making its runtime sub-linear in the size of the large tensor one is approximating. Related results concerning the so-called Tensor Restricted Isometry Property (TRIP) will also be discussed.