Jim Webber explores graph data analytic techniques using social graph properties inspired by anthropology and sociology, extracting online business intelligence from graph matching.
Peter Bell presents several patterns for modeling and retrieving data from graph databases using Neo4j in his examples.
Axel Morgner compares different open source CMS’s and outlines the benefits of implementing one using a graph database.
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
Peter Bell introduces 4 NoSQL categories –Key-Value, Document, Column, Graph - and explains how one can use Spring Data to work with such data stores.
Jim Webber talks about the data of these days, how integrated data looks, how to model it using actual data stores and the implications of this modeling.
Ian Robinson introduces Neo4J, a graph database, discussing how it can be used to store and work with data associated with Doctor Who.
Justin Dearing introduces MongoDB, and shows how to interact with Mono via the official 10gen driver. Techniques for handling business logic in application code, such as LINQ are discussed.
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.
Emil Eifrem overviews the trends leading to NOSQL, and four emerging NOSQL solutions. He also explains the internals of a graph database and an example of using Neo4j – a graph DB - in production.