Case Study: Real-time Recommendations with a Graph Database Whether you want to be Facebook or are selling shoelaces online, if you have users then you have a social graph. Reveal the hidden graph in your data by storing key elements in a graph database, focusing on the relationships between records rather than the aggregation of records. Andreas Kollegger looks at how a large European social network added real-time recommendations to their service with a hybrid of MySQL and Neo4j, covering:

  • Graph concepts refresher: whiteboard friendly modeling
  • Polyglot persistence: storing the right data in the right place
  • Graph algorithms: recommendations for any domain