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RDF has constantly gained attention for data publishing due to its flexible data model, raising the need for distributed querying. However, existing approaches using general-purpose cluster frameworks employ a record-oriented perception of RDF ignoring its inherent graph-like structure. Recently, GraphX was published as a graph abstraction on top of Spark, an in-memory cluster computing system. It allows to seamlessly combine graph-parallel and data-parallel computation in a single system, an unique feature not available in other systems. In this paper we introduce S2X, a SPARQL query processor for Hadoop where we leverage this unified abstraction by implementing basic graph pattern matching of SPARQL as a graph-parallel task while other operators are implemented in a data-parallel manner. To the best of our knowledge, this is the first approach to combine graph-parallel and data-parallel …