DataStax Launches DataStax Enterprise Graph for Real-time Graph Database

DataStax Enterprise Graph is a scale-out graph database built for cloud applications that need to manage highly connected and complex data

Apac CIOOutlook | Friday, April 22, 2016

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SANTA CLARA, CA: DataStax, a provider of database software and cloud applications, announces DataStax Enterprise Graph, to add graph database in its portfolio.

DataStax Enterprise Graph is a scale-out graph database built for cloud applications that need to manage highly connected and complex data. The open source offering is built on the foundation of Apache Cassandra, Apache TinkerPop, and Titan-a graph database by Aurelius.

The new enterprise graph product has been fully integrated into the DataStax Enterprise product set, giving customers an integrated graph option in addition to key-value, tabular and JSON document models it offered previously.

DataStax Enterprise Graph is a complete solution for developing and managing graph functionality in cloudapplications and includes DataStax Enterprise Server, DataStax OpsCenter, DataStax Studio, DataStax Drivers.

DataStax Enterprise Server to add advanced graph database functionality, which includes an adaptive query optimizer, automatic graph data partitioning, a distributed query execution engine, and graph-specific index structures all designed to increase performance for online graph applications. DSE Graph is built with TinkerPop, which is the industry standard framework and language for graph databases.

DataStax OpsCenter, is updated to provide full provisioning, management and monitoring for DSE Graph.

DataStax Studio, a new web-based solution that helps developers visualise graphs and write/execute graph queries.

DataStax Drivers are available for all popular development languages and enhanced to support the Gremlin graph language in addition to CQL and DSE Analytics/Search API's.

Graph database offers the most effective tool for handling recommendation and personalization tasks in an application and making key decisions from the value found in data. In a complex and highly interrelated network of users, entities, transactions, events and interactions, a graph database can help determine which interaction is fraudulent, poses a security risk or compliance concern.