You've written the JOIN. Then the JOIN on the JOIN. Then the subquery that made your DBA question your life choices. Relational tables are brilliant for storing data — but they were never designed to model relationships as first-class objects. That's the job of a knowledge graph.

In this session, we'll build intuition and hands-on familiarity with graph data modeling using Python and R programming, with a side-by-side look at other tools that aid the visualization. You will see exactly where the structural difference lives. We'll connect graph thinking to a topic you're already hearing about — Retrieval-Augmented Generation — and show why GraphRAG produces smart AI results.

By the end of the session, you'll know:

How to model a dataset as nodes and edges instead of rows and columns
When a graph database earns its place in your stack (and when it doesn't)
How to visualize a graph for teammates who don't speak graph
How to add graph thinking to your next sprint without rebuilding your architecture