Neo4j has announced the availability of Neo4j for Graph Data Science, a data science environment that helps data scientists utilize predictive relationships and network structures to answer questions. It combines a native graph analytics workspace and graph database with graph algorithms and graph visualization. The workspace enables native graph creation and persistence for shaping in-memory graphs, with graph visuals in Neo4j Bloom helping teams to explore results quickly.
Neo4j offers a graph database that helps organizations make sense of their data by revealing how people, processes and systems are related. Neo4j natively stores interconnected data so it’s easier to decipher data. The property graph model also makes it easier for organizations to evolve machine learning and AI models. The platform supports high-performance graph queries on large datasets as well.
Key features of the new data science workspace include optimized, parallel algorithms that run over tens of billions of nodes and relationships, production features like deterministic seeding, and a friendly data science experience with logical memory management. Neo4j for Graph Data Science also touts intuitive APIs and extensive documentation, as well as native integration with the company’s flagship graph database.
In a media statement about the release, Neo4j’s Lead Product Manager and Data Scientist Alicia Frame said: “A common misconception in data science is that more data increases accuracy and reduces false positives. In reality, many data science models overlook the most predictive elements within data – the connections and structures that lie within. Neo4j for Graph Data Science was conceived for this purpose – to improve the predictive accuracy of machine learning, or answer previously unanswerable analytics questions, using the relationships inherent within existing data.”
Neo4j released its BI Connector for Tableau, Looker, TIBCO Spotfire, Oracle Analytics Cloud and MicroStrategy late last month. The tool makes connected data insights accessible in real-time to users without the need for scripting or code. The connector is also fully supported and ready for in-enterprise production deployment.