Description:

Many applications today handle data that is deeply associative, i.e. structured as graphs (networks). The most obvious example of this is social networking sites, but even tagging systems, content management systems and wikis deal with inherently hierarchical or graph-shaped data.

This turns out to be a problem because it is difficult to deal with recursive data structures in traditional relational databases and many NoSQL stores alike. For example, in an RDBMS each traversal along a link in a graph is a join, and joins are known to be very expensive.

A graph database uses nodes, relationships between nodes and key-value properties instead of tables to represent information. This model is typically substantially faster for associative data sets and uses a schema-less, bottoms-up model that is ideal for capturing ad-hoc and rapidly changing data. This session will introduce an open source, high-performance, transactional and disk-based graph database called "Neo4j" (http://neo4j.org), which frequently outperforms relational backends with >1000x for graph-shaped data.