RDF graph databases provide important advantages when it comes to data management: easy interlinking of entities; agile integration of data coming from heterogeneous data sources and described by different schemata; the ability to infer new, implicit facts based on the entities stored in the database and the relations between them.

The SPARQL query language provides the means for expressive queries over RDF graph data. In some cases though, it is a challenge for RDF database engines to quickly provide answers to very complex SPARQL queries. Complex queries include those with lots of patterns, filters, aggregates and full-text searches running over very large volumes of RDF data.

In many cases, it is more efficient to have an external search engine (i.e. Lucene, Solr, Elasticsearch) or a NoSQL database (such as MongoDB or Cassandra) handle part of the query filtering process for full-text search or faceted search queries. This reduces the workload placed on the RDF engine itself. GraphDB™ Connectors provide this solution, the transparent integration of an external distributed data engine (search engine, NoSQL database) with an RDF database. All data queries and updates are performed via standard SPARQL interfaces, but the SPARQL query processor offloads some of the complex query filtering operations to an external distributed data engine.

In this presentation we will discuss the design of the GraphDB™ Connectors as well as the practical benefits and some of the most common use cases.