DataStax Enterprise Graph

The only scalable real-time graph database

DataStax Enterprise Graph is the first graph database fast enough to power customer facing applications, capable of scaling to massive datasets and advanced integrated tools capable of powering deep analytical queries. Because all of DataStax Enterprise is built on the core architecture of Apache Cassandra, DataStax Enterprise Graph can scale to billions of objects, spanning hundreds of machines across multiple datacenters with no single point of failure.

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Built for Cloud Applications

Graph databases make sense of highly connected data, but a graph database by itself isn’t enough to satisfy the needs of modern cloud applications. The power of a graph database can only be fully realized when paired with the functionality of advanced analytics, real-time indexing and search. With competitive offerings each of these requirements would need to be satisfied by an individual point solution. Because each set of requirements is solved by a separate solution it’s up to the user to manage and keep all of these separate solutions in sync. This complexity greatly increases the total cost of ownership, and time to market. With DataStax Enterprise the capabilities required to build a modern cloud application are vertically integrated, write your data once and it’s accessible in real-time via graph, search, and analytics.

Multi-Model Capabilities

DataStax Enterprise Graph is part of the DataStax Enterprise multi-model platform, which provides support for key-value, tabular, JSON/Document, and graph data models. Because data from all models are stored in a single persistence layer, each data model inherits all of the benefits of Apache Cassandra as well as the enterprise grade functionality of DataStax Enterprise.

Enterprise Ready

DataStax Enterprise Graph incorporates all of the enterprise-class functionality found in DataStax Enterprise. These benefits include advanced security protection, built-in analytics and enterprise search functionality, and visual management, monitoring, and development tools.

Also, as part of the DataStax Enterprise platform, DataStax Enterprise Graph includes around-the-clock support from the graph experts at DataStax, which delivers the confidence needed to run and maintain production graph systems at scale. Support also includes formal end-of-life policies, certified software updates, hot-fixes, and bug escalation privileges.

Commitment to Open Standards

Built with Apache TinkerPop

Apache TinkerPop is the industry standard open source graph computing framework that offers graph computing capabilities to database systems. DataStax Enterprise Graph is compatible with any Apache TinkerPop 3.0 database. Part of Apache TinkerPop is Gremlin, the standard language for graph databases. Being Apache TinkerPop-enabled, DataStax Enterprise Graph is able to include sophisticated, standardized graph computing features to its core foundation and avoids proprietary vendor lock in.

DSE Integrated

DataStax Enterprise Graph is fully-integrated with the DataStax Enterprise data platform, powered by the industry’s best version of Apache Cassandra. This means your Graph powered insight can leverage the best of DSE Analytics and DSE Search while remaining always-on with effortless scale.

Inspired by Titan

The design of DataStax Enterprise Graph is inspired by the open source Titan graph database, but goes far beyond the scale-out capabilities of Titan by deeply integrating with Apache Cassandra. This integration provides key improvements over the Titan core such as automatic data consistency and additional commercial software functionality.
Who Uses DataStax Enterprise Graph?

Common Graph Use Cases

Graph databases are well suited for large data sets with numerous and highly complex relationships. Graph databases make it easy to discover, explore and make sense of these relationships.

Master Data Management / Customer 360

A company must understand the data relationships across its multiple business units to create a holistic view of its customers or products. A graph model is the best way to consolidate the disparate data for use by both BI tools and other business applications. An example of this application include using graph to understand the various ways a customer interacts with your company, what types of accounts do they have, what services are they using and what are the various identities they use across the separate properties both virtual and physical.

Other examples of master data management include product catalogs and product lifecycle management (PLM). Product catalogs and PLM systems often have complex hierarchical structures and are overlaid by taxonomies to capture the composition of relationships. Understanding these relationships, being able to immediately grasp the impact of a change in a supplier relationship, or being able to quantify the impact of a recall are all capabilities that are made easy through the use of graph technology.

Recommendation and Personalization

Almost all enterprises need to understand how they can quickly and most effectively influence customers to buy their products and recommend them to others using components in a cloud application such as recommendation, personalization, and network (people or machines) analysis engines. A graph is well suited to these and similar analytical use cases where recommending products, next actions, or advertising based on a user’s data, past behavior, and interactions are important.

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 entity, transaction, or interaction is fraudulent, poses a security risk, or is a compliance concern. In short, a graph database assists in finding the bad needle in a haystack of relationships and events that involve countless financial interactions.

Internet of Things

The Internet of Things (IoT) is yet another domain area that is replete with graph-sized problems. The IoT use cases most commonly involve devices or machines that generate time-series information such as event and status data. A graph works well in this case because the streams from individual points create a high degree of complexity when blended together. Further, analytics involved in tasks such as root-cause analysis, involve numerous relationships that form among the data elements and tend to be of much greater interest when examined collectively than reviewed in isolation.
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