This paper is an overview of recommender systems in the era of Big Data. We highlight prevailing recommendation algorithms and how they have been adapted to operate in parallel computing environments. These include traditional parallel computing environments, such as OpenMP and MPI, and also more recent distributed computing engines, such as MapReduce and Spark. Within the recommender systems context, we focus our discussion on scaling up two popular approaches, namely nearest neighbor and latent factor based recommendation.