When most data scientists think of Graph Algorithms, they think of batch analytical processes running computation on a graph in R/iGraph, Gephi, etc. These tools certainly provide great insight into your data, but they don't provide the ability for applications to make real-time decisions based on these algorithms. For that, you need to store your data in an OLTP.
Neo4j is a graph database which combines the ACID data guarantees you expect from SQL, with the schema flexibility you expect from NoSQL, and the performance for traversing connected data that you expect from a native graph database. It excels at querying your data using graph patterns. More recently, we've added the ability to run graph analytics on top of the database to make real-time decisions.
Why settle for your classic data analysis tools and making decisions in offline processes? This session will be a fast-paced intro to the power of graphs for transactions, storage, traversal, and analysis: Graph Algorithms on ACID.
Oh, and if you have your data in Spark, I'll also give a brief preview of Cypher on Apache Spark!
Engineer and Director of Developer Relations (Neo4j)