Paul King reviews the features in Groovy which make it easy to work with databases - Groovy SQL, datasets -, and working with NoSQL databases such as MongoDB and Neo4J.
Yan Cui shares lessons learned using Neo4j to model the in-game economy of the "Here Be Monsters" game and automate the balancing process.
James Richardson, Nat Pryce discuss some of the challenges faced using Neo4J for interactive analysis of large data imports (80K nodes, 150k relationships) and how they overcame them.
Michael Hunger and Lorenzo Speranzoni show how easy it is to get started with Spring Data Neo4j using Spring Boot.
Ian Robinson takes a look at how size, structure and connectedness have converged to change the way we work with data, showing some new opportunities with connected data illustrated with graph search.
Volker Pacher, Sam Phillips present key differences between relational databases and graph databases, and how they use the later to model a complex domain and to gain insights into their data.
Ian Robinson discusses graphs data structures, some of the queries that can extract data from them, and tools and techniques to work with graph data.
Mridula Jayaraman shares from her experience developing a next generation sequencing solution used to customize cancer treatment based on patient's genetic makeup.
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
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.