Querying & Visualizing Graphs in Postgres with Apache AGE

Apache AGE lets you store graph data inside Postgres – but the usual database tools weren't built to show you what graphs look like. You can query nodes and edges by embedding Cypher inside SQL, but making sense of what comes back requires graph visualization.

This talk covers what works and what fails when visualizing AGE query results. I'll walk through common problems: layouts that obscure rather than reveal, node-link diagrams that become unreadable at modest scale, and interaction patterns that break when graphs get dense. Then I'll show techniques that hold up – including when to reach for force-directed layouts versus layered or topology-aware approaches.

The graph-in-Postgres model means you can pick the right model for the problem: relational for aggregations and filtering, graph for traversals and pattern matching. But graph query results need visualization approaches designed for connected data – techniques that reveal structure rather than flatten it back into rows. Drawing from fifteen years of building graph visualization tools, I'll show what that looks like in practice.