At GraphAware, we live and breathe Neo4j. For three years, we have been helping customers around the world embrace this amazing technology as a solution to many interesting problems. Mainstream applications of graphs, such as real-time recommendations, fraud detection, impact analysis, and graph-aided search, have been getting a lot of media attention.
At GraphAware, we are very excited about the recently released Neo4j 2.2 and would like to share some info about where you can meet us in the next few weeks and months. Come and see us for a chat and learn something new about Neo4j and Graph Databases!
Over the last few months, GraphAware, Neo4j, and Pivotal engineers have been working on a ground-up reimplementation of Spring Data Neo4j (SDN) that is server-first and Cypher-centric. Today we are very excited to announce the first milestone of the new Spring Data project for Neo4j.
There is no better way to start 2015 than to learn something new. In the wake of two recent major announcements (here and here), Neo4j is as hot as ever, so it might well be the next skill you pick up or improve. Here’s a list of Neo4j events organised by GraphAware around the world in the next few weeks. We’ll be delighted to see you there!
In this post, we’d like to introduce the first version of the GraphAware Neo4j ChangeFeed - a GraphAware Runtime Module that keeps track of changes made to the graph.
With MERGE set to replace CREATE UNIQUE at some time, the behavior of MERGE can sometimes be tricky to understand.
Efficient counting of relationships in Neo4j was the cornerstone of my Master Thesis and the reason the very first GraphAware Framework module called the Relationship Count Module was born. The improvements in Neo4j 2.1 around dense nodes and the addition of getDegree(…) methods on the Node interface made me eager to do some benchmarking around relationship counts again.
In the last post of our “Neo4j Modelling for Beginners” series, we looked at bidirectional relationships. In this post, we compare the implications of qualifying relationships by using different relationship types versus using relationship properties.
Transitioning from the relational world to the beautiful world of graphs requires a shift in thinking about data. Although graphs are often much more intuitive than tables, there are certain mistakes people tend to make when modelling their data as a graph for the first time. In this article, we look at one common source of confusion: bidirectional relationships.
S laskavým svolením organizátorů konference WebExpo si dovoluji veřejně zpřístupnit záznam své přednášky o Neo4j. Enjoy!