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• Geometry-driven tensorization coupled with the tensor-train decomposition yields a hierarchical compression scheme. • A new streaming data compression scheme, in which batches of simulation data can be efficiently incorporated to a compressed representation as soon as they are generated. • Demonstration of high compression ratios (1 K-10 M fold reductions) of discrete element vehicle-over-soil simulation dataset. We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. We show that geometry-driven "tensorization" coupled with the TT decomposition (known as quantized TT) yields a hierarchical compression scheme, achieving high compression ratios for key variables in these DEM datasets. [ABSTRACT FROM AUTHOR]