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Many Big Data applications in business and science require the management andanalysis of huge amounts of graph data. Previous approaches for graph analyticssuch as graph databases and parallel graph processing systems (e.g., Pregel)either lack sufficient scalability or flexibility and expressiveness. We aretherefore developing a new end-to-end approach for graph data management andanalysis based on the Hadoop ecosystem, called Gradoop (Graph analytics onHadoop). Gradoop is designed around the so-called Extended Property Graph DataModel (EPGM) supporting semantically rich, schema-free graph data within manydistinct graphs. A set of high-level operators is provided for analyzing bothsingle graphs and collections of graphs. Based on these operators, we propose adomain-specific language to define analytical workflows. The Gradoop graphstore is currently utilizing HBase for distributed storage of graph data inHadoop clusters. An initial version of Gradoop has been used to analyze graphdata for business intelligence and social network analysis.

GRADOOP: Scalable Graph Data Management and Analytics with Hadoop