Mark Needham shows how a meetup recommendation engine using Neo4j and Clojure can be built from scratch, combining content-based and collaborative filtering using Cypher and Clojure.
Ian Bull introduces Node4J and explores the performance characteristics and highlights the tools that help one develop, debug and deploy Node.JS applications running directly on the JVM.
Jim Webber talks about several kinds of fraud common in financial services and how each decomposes into a straightforward graph use-case. He explores them using Neo4j and Cypher query language.
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.