Discovering the structure and content of an RDF graph is hard for human users, due to its heterogeneity, complexity, and possibly large size. One class of tools for this task are structural RDF graph summaries, which allow users to grasp the different connections between RDF graph nodes. RDFQuotient graph summaries are a brand of structural summaries we developed. They are usually very compact, making them good for first-sight visual discovery. Existing algorithms for building these summaries are centralized, and require the graph to fit in memory. Going beyond, in this work we present novel algorithms for building RDFQuotient summaries in a parallel, shared-nothing architecture. We instantiate our algorithms to Apache Spark platform; our experiments demonstrate the merit of our approach.