In general, data sets are growing larger and larger, and handling related issues is topic of big data. Similar trends and tendencies are evident in data envelopment analysis (DEA). DEA is a well-known instrument for determining the efficiencies of decision-making units (DMUs), applying linear programming. Still, as we will show, DEA suffers notably from the curse of dimensionality. Therefore, we propose improved decomposition-based algorithms involving different termination criteria and multithreading to address this issue. For some of these criteria, we prove the convergence of the algorithm; to the best of our knowledge, we are the first to prove this. Ultimately, from a computational point of view, we study the performance of the new big data strategy by an extensive numerical analysis, thus demonstrating the algorithm’s scalability.