The advent of multicore systems and distributed frameworks enables distributed strategies to address challenges in large-scale but divisible problems by decomposing them into small ones, processing the corresponding sub-solutions and aggregating these sub-solutions into the final results. However, dynamic online detection of data races from traces of multithreaded programs is challenging to be parallelized due to their inherent historic event sensitivity and incremental inference of happens-before transitive closure. To examine the extent of such detection to be run on Big data infrastructure, in this paper, we present BlockRace, a novel dynamic block-based data race detection technique, which precisely detects data races in traces of multithreaded programs and check pairs of events blocks in parallel using its novel strategy. We evaluate BlockRace on 18 programs, and the results show that BlockRace achieves 1.96x to 5.5x speedups compared to its sequential counterpart. To the best of our knowledge, BlockRace is the first technique to realize effective and efficient precise data race detectors in Big Data environments.