Learn what works and what fails when visualizing Apache AGE graph results in Postgres. Christian Miles of gdotv presents his talk “Querying & Visualizing Graphs in Postgres with Apache AGE” at POSETTE: An Event for Postgres 2026. Abstract: 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.

Christian Miles specializes in graph database visualization and analytics. His work spans fraud detection, master data management, and cybersecurity deployments at enterprise scale. At gdotv, he bridges the gap between database theory and practical implementation – onboarding customers through complex query challenges, translating pain points into product features, and designing visualization interfaces that make graph data accessible to technical and non-technical users alike.

► Video chapters:
⏩ 00:00 – Music & introduction
⏩ 01:11 – What is a graph, really?
⏩ 03:19 – Exploring the graph database landscape
⏩ 04:14 – Choosing the right graph database
⏩ 05:23 – Graph query languages explained
⏩ 06:32 – Knowledge graphs and real-world use cases
⏩ 08:21 – Bringing graphs into Postgres
⏩ 09:28 – Apache AGE architecture and benefits
⏩ 10:43 – Demo: querying graphs in Postgres
⏩ 13:01 – Why visualization matters for graph data
⏩ 14:52 – The pitfalls of “hairball” graphs
⏩ 16:26 – How force-directed layouts work
⏩ 18:30 – Structured layouts for clarity
⏩ 19:29 – Exploration vs explanation in visualization
⏩ 21:39 – Final takeaways on AGE and visualization

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Microsoft Developer

A talk covering Apache AGE, a Postgres extension that brings graph database capabilities to relational data stores using the openCypher query language. The speaker walks through the graph database landscape (native databases, extensions, query engines), compares approaches like raw SQL CTEs, pgRouting, and Apache AGE, and explains how AGE embeds Cypher queries inside SQL. The second half focuses on graph data visualization: the pitfalls of force-directed 'hairball' layouts, when to use structured/hierarchical layouts instead, and the distinction between exploratory and explanatory visualization. A practical example uses Postgres commit history as a graph data model.

Querying & Visualizing Graphs in Postgres with Apache AGE | POSETTE: An Event for Postgres 2026