Modern Graph Databases

The last decade has seen major changes in the way we work with highly interconnected data sets. Dozens of technologies came and went, replaced by completely new tools. Now, in the age of recommendation systems and AI, storing and querying data as a graph efficiently is more important than ever. Semantic graphs and clustering algorithms power the latest generation of agentic AI tools. The industry has come up with completely new ways of working with graph data, which we’re going to take for a test drive in this talk! We’ll check out what changed in Neo4j, the only major graph database that is still with us after 10 years. Then we’ll discuss in-process analytical graph databases, which is an idea that didn’t even exist until just a few years ago. In particular, we’ll test KuzuDB and Memgraph with some demos and discuss when to use them. We’ll look at graph processing engines, like PuppyGraph, and see what makes them different from databases. Finally, to understand why graph databases have become more important than ever in the age of LLMs, we’ll build an AI agent augmented by a knowledge graph served from FalkorDB.