[lmeyerov]

We see them for smaller transaction extracts, but most vendors here are too expensive for most teams at the scale you are likely thinking. Instead, we see log, kv, dwh, etc systems -- think elastic, Clickhouse, databricks, and the like. Some teams will stage daily batches into a graph DB, but having an expensive second system of records masquerading as a compute engine gets frustrating.

Separating scalable transaction storage from graph compute opens a lot. The new Google Spanner Graph launch is interesting here, and for OSS, we have been working on GFQL so you can bounce between vectorized Python dataframe mode on small graphs to 1B row GPU batches for bigger graphs.