Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
Jim Webber explains how to understand the forces and tensions within a graph structure and to apply graph theory in order to predict how the graph will evolve over time.
Volker Pacher explains why Shutl chose Neo4j when faced with the need of building a new API meant to support business growth, the challenges met during implementation and solutions applied.
Ian Robinson discusses the complexity of highly connected data and how graph databases can help, illustrating the talk with practical examples implemented using Neo4j.
Stefan Armbruster discusses building a Grails application with a graph data store based on Neo4j and sharing insight based on his own experience using such a system in production.
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.