Neo Technology Announces Release of Neo4j version 2.0 Graph Database Platform; Notes Use of Neo4j By Zephyr Health

Neo Technology today announced the release in general availability of version 2.0 of its graph database technology platform, Neo4j. The Neo4j graph database platform enables users to find connections between and amongst data points in high velocity and variety datasets “where the relationships between constituent data points are so numerous and dynamic that they cannot easily be captured within a manageable schema or relational database structure. Graph databases contain “nodes” or “vertices” and “edges” that indicate relationships between the different vertices/nodes.” Neo4j 2.0 features the addition of three notable features: (1) labels are now part of the data model and allow data scientists and developers to tag and index data for the purpose of more effectively understanding relationships between datasets; (2) enhancements to Cypher, the declarative query language used for the development of Neo4j graph applications; and (3) an interactive browser and query environment with a visual interface for data discovery.

Today, Neo Technology also announced that Zephyr Health is using Neo4j to power its cloud-based analytics platform:

The Zephyr analytics platform allows pharmaceutical makers, medical device manufacturers, and other health care customers, to discover unique connections across their data that can advance their R&D, clinical trials, and marketing. For instance, Zephyr’s engine helps pharmaceutical companies find the right doctors for a clinical trial by linking private and public data — such as specialty, geography, and clinical trial history.

Zephyr Health chose the Neo4j platform as the basis for its big data analytics environment because of its need to make connections between disparate data sets in real-time, as well as the highly dynamic nature of its datasets about hospitals and physicians. According to Neo Technology’s press release, Neo4j has effectively scaled in conjunction with the exponential growth of Zephyr’s datasets and delivered a solution that allows Zephyr’s business users to “be their own data scientists” by way of its data discovery and interactive browser functionality.

Zephyr Health’s adoption of Neo4j represents just one data point on a larger canvas of enterprise adoption of Neo4j as illustrated below:

The verticals from left to right illustrate Neo4j’s adoption in industries over and beyond verticals that traditionally use graph databases such as social media, online data and transportation. The larger point here is that, graph database technology—whether via Apache Giraph, Neo4j or otherwise—has arrived within the enterprise as a means of managing relationships between richly associative, dynamic, multivalent datasets in ways that enable connections and the inference of probabilistic relationships between nodes within the graph in ways that exceed the analytic capabilities of relational databases. The industry should expect use cases such as Zephyr Health’s elaboration on its use of Neo4j to proliferate as users of graph database technologies becoming increasingly comfortable explaining its business value and significance.

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