Graphs at Scale: From a node to fetching real-time insights in milliseconds

We work with connected data every day: followers, transactions, dependencies, routes, recommendation systems – and most people just instinctively pull out a relational database and begin coding JOINs. In this talk, we're going to do a ground-up introduction to graph databases and graph engines: what they are, how they think about data differently, and where they really excel at scale.

The talk will introduce - What is a graph database, and how is it different from a graph engine? Why does traversing relationships feel intuitive in a graph but excruciating in SQL? We're going to prove all this with diagrams, and actual query comparisons before we ever discuss any actual application problem. Then we're going to build our way up through the data model, query languages (openCypher), OSS ecosystem (Neo4j, Apache AGE, Memgraph), and finally, we're going to bring it all home in the context of my actual work in financial risk and fraud detection.