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DataStax Enterprise (DSE) Graph, is a scale-out graph database built for cloud applications that need to manage highly connected data. Built on the foundation of Apache Cassandra and Apache TinkerPop, the open source graph computing framework, DSE Graph delivers continuous uptime, predictable performance and scalability for modern systems dealing with complex and constantly changing data.
DataStax Enterprise Graph is inspired by the open source Titan graph database. Aurelius, the team behind Titan was acquired by DataStax in 2015 and the team has built a new set of software that extends significantly beyond the basic capabilities of Titan while still maintaining backwards compatibility. This backwards compatibility allows existing Titan and other users of TinkerPop supported graph databases to migrate with little or no effort. DSE Graph inherits Cassandra’s key benefits including constant uptime, read/write/active-everywhere functionality, linear scalability, predictable low-latency response times and operational maturity. DSE Graph also incorporates enterprise-class extensions found in DataStax Enterprise including advanced security, built-in analytics, enterprise search, visual management monitoring and development tooling.
DataStax Enterprise Graph is a complete solution for developing and managing graph functionality in cloud applications and includes:
DataStax Enterprise Server: delivers advanced graph database functionality that 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: updated to provide full provisioning, management and monitoring for DSE Graph. DataStax Studio: a new web-based solution that helps developers visualize graphs and write/execute graph queries. DataStax Drivers: available for all popular development languages and enhanced to support the Gremlin graph language in addition to CQL and DSE Analytics/Search API’s.
There are a variety of use cases where a graph database is a better fit than other database management systems including relational or general NoSQL database systems.
• Master Data Management: A company must understand the data relationships across its multiple business units to create a holistic view of its customers. A graph model consolidates disparate data for use by both business intelligence tools and business applications.
• Recommendation and Personalization: Enterprises need to understand how to quickly and effectively influence customers to purchase their product. Graph analysis is the most effective tool for handling recommendation and personalization tasks in an application and making key decisions from the value found in data.
• Security and Fraud Detection: 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.
• IoT and Networking: As IoT use cases commonly involve devices or machines that generate time-series information such as event and status data, graph databases work well because the streams from individual points create a high degree of complexity when blended together. Additionally, analytics involved in tasks such as root cause analysis, involve numerous relationships that form along the data elements and tend to be of much greater interest when examined collect.
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