Knowledge graphs and BI
Most graph databases were built for traversal, not aggregation. That distinction matters enormously the moment you try to do anything resembling BI, reporting or dashboarding on top of them.
The problem is structural because BI tools like Tableau, Microsoft Power BI, Metabase, Looker… all speak SQL. They expect rows and columns, group-by, window functions and a query planner that knows how to optimize count-distinct over millions of records. Graph databases speak Cypher, Gremlin, or SPARQL. Brilliant for path finding but terrible for something like “give me revenue by product category over the last quarter”.
The classic workaround is to replicate your graph into a relational warehouse and query there. Which works, right up until your graph changes and your ETL pipeline needs to be updated.
Neo4j’s NeoDash is the notable exception. It was built specifically to render graph data as dashboards, all driven by Cypher queries against a live Neo4j instance. No translation layer, no sync lag. It understands that a graph query can return a table if you shape it right, and it lets you do exactly that. For Neo4j shops, it genuinely closes the gap. Very cool.
If you’re not a Neo4j shop, you’re on your own. Hybrid databases like ArcadeDB and SurrealDB have an advantage here. Both support multiple query languages natively (including SQL) over the same underlying graph data. That means your graph nodes are also SQL-queryable rows. Standard BI connectors can speak directly to them. You don’t need to export, replicate, or translate. It’s an overlooked advantage of hybrid systems.
Of course, it’s not a perfect story. SQL over a graph is still an impedance mismatch under the hood. Joins don’t think in paths and you lose the expressiveness that made you choose a graph model in the first place. For dashboards, KPIs, and operational reporting, it’s often good enough. The deeper issue is that the tooling ecosystem hasn’t caught up with the data model. Most organizations adopting knowledge graphs end up maintaining two parallel stacks: one for graph intelligence, one for reporting. Until BI tools natively understand graph primitives or until graph databases get serious about analytical query planning this friction isn’t going away.