From first graph algorithm experiment to production with Aura Graph Analytics

Many data teams have the same conversation about graph analytics: “That’s the fraud detection thing, right?” Then they go back to their pipelines, assuming that graph analytics isn’t relevant to their use cases or the data they’re working with. It’s a reasonable shortcut, but a limiting one. Use cases like fraud detection, entity resolution, and customer 360 dominate the conversation because they’re proven, high-stakes, and relevant across industries, which is exactly why they overshadow a lot of other areas where graph algorithms would give you genuinely useful signals. Most datasets have a connectivity aspect that tabular approaches fail to surface. These aspects don’t always map to a textbook graph analytics use case, and that’s fine. Today, you can get a concrete answer on where graph analytics adds value to your data in minutes, without needing deep algorithm expertise — and that’s exactly where this article starts. From there, we’ll cover how to run your first experiments, get your data in and out efficiently, and eventually turn the whole thing into a production pipeline. The tool you’ll be using throughout this article is Aura Graph Analytics (AGA), Neo4j’s on-demand compute environment for running graph algorithms at scale. It’s built on top…