Entity Resolution (ER) is the process of locating records that represent the same real-world entity, within a single or across different datasets. Various techniques are used for the implementation of the ER process, like blocking, filtering, and matching, in order to improve its performance and effectiveness. However, ER faces new challenges in the age of big data analytics, since traditional methods of handling data have not proved very efficient. In this work, we study the ER process and focus on further improving the efficiency of blocking techniques by leveraging the capabilities of big data parallel processing platforms. Specifically, we introduce parallelized suffix array blocking with Bloom filters using Apache Spark and explore its performance compared to a serial implementation. Our evaluation results show significant improvement with the parallel approach being up to 5 times faster.