A distributed fault tolerant nondestructive evaluation (NDE) data reduction framework is proposed in which large NDE datasets are mapped to thousands to millions of parallel, independent processes running on a mobile device, standard computer, or a networked cluster of machines. Each process scans a subset of the data for flaws and as independent entities are unaffected by errors in fellow processes or system failures. If a process fails, only its work is lost as the system continues to process the data; the work lost is immediately picked up by another process. The results of the parallel analyses are compiled back to the original dataset with structural flaw indicators. Phase I efforts are devoted to designing the framework and providing a proof of concept prototype able to automatically detect defects in NDE data.