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