Michael Hunger discusses graph databases and the need for them in the larger context of NoSQL data stores, introducing Spring Data, Neo4j, and Spring Data Neo4j.
Rick Bullotta and Emil Eifrem discuss how to use graph databases to model the real world, people, systems and things, talking advantage of the relationships between various data elements.
Borislav Iordanov presents the architecture of HyperGraphDB, a special type of store based on hypergraphs – graphs with edges pointing to an arbitrary number of nodes and to other edges, comparing it with other graphs databases and its relationship to other NoSQL stores.
Emil Eifrem overviews the trends leading to NOSQL (Not Only SQL), and the four emerging NOSQL solutions: key-value stores, plus column, document and graph databases. He also explains the internals of a graph database and an example of using Neo4j - a graph database - in production.
This presentation covers the definition of a graph database (information structured as mathematical graphs with nodes, relationships and properties) and their advantages when dealing with data that is difficult to fit in static tables, is rapidly evolving, or that has a lot of optional attributes. The flexibility of graph databases better support agile development and schema evolution.