While Neo4j has a smattering of data science use cases, the majority of its customers use the database for transaction processing. The company has offered the capability to run some graph algorithms. But production machine learning use cases typically require data scientists to spend much of time working with open source software that’s external to Neo4j, as well as extensive data engineering efforts to construct a data pipeline to that external system.
Basically, doing data science with Neo4j data has been painful, expensive, and not scalable, according to Neo4j’s lead product manager Alicia Frame. But that all should change with today’s launch of Neo4j for Graph Data Science, which Frame says will make doing data science on Neo4j much easier, less painful, and more scalable than before.