DataStax Announces DataStax Enterprise Graph, the Only Scalable Real-Time Graph Database

  • Powers cloud applications that need to manage complex and highly connected data
  • Graph-specific use cases include master data management, recommendation & personalization, security & fraud detection, IoT & networking
  • Part of the only multi-model, scalable, distributed database management platform that includes key-value, tabular, JSON/document and graph data capabilities
  • DataStax Enterprise Graph early access program participants include Cambridge Intelligence and Linkurious

SANTA CLARA, CA – April 12, 2016DataStax, the leading provider of database software for cloud applications, today announced DataStax Enterprise (DSE) Graph, 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. DSE Graph will be generally available in Q2.

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Perspectives on the News

“We evaluated DataStax Enterprise Graph against traditional databases for some of our large banking customers and found that DSE Graph improves performance by an order of magnitude when working with data sets that include a large number of nodes and relationships – on use cases such as client data for financial services,” said Anil Gurnani, Banking and Capital Markets Solution, Mphasis.

“We’re seeing growing demand from customers who are ready to utilize graph databases for their cloud applications, but they need a truly scalable and production-ready platform that has the enterprise-class functionality required to be successful,” said Robin Schumacher, VP of Products, DataStax. “DSE Graph provides them exactly what they need as it’s built on the foundation of Apache Cassandra and imports all of DSE’s enterprise functionality, including advanced security, built-in analytics and enterprise search functionality, visual management and monitoring, and much more.”

As stated in the July 25, 2015 report, “Making Big Data Normal With Graph Analytics,” authored by Gartner analysts Mark Beyer and Nick Heudecker:

“Graph is an excellent method of evaluating, expressing and analyzing previously unrecognized relationships in data. Instead of examining and analyzing data as a set of discrete and unrelated atomic elements, graph allows for the exploration of the frequency, strength and direction of relationships in data.” The report also advises, “graph can be used by the business when traditional analytics fails to identify what is causing a business process to behave in unexpected ways. It also supports networks of people, processes and machinery as well as forms multifaceted recommendation engines, multiple transaction analysis engines including fraud models, and more.”1

Introducing DataStax Enterprise Graph

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.

Graph-Specific Use Cases

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 collectively versus in isolation.

Solving Business Problems with Multi-Model Support

As stated in the August 4, 2015 report, “Market Guide for NoSQL DBMSs,” authored by Gartner analysts Nick Heudecker, Merv Adrian and Etisham Zaidi2, “the future of DBMS architectures and deployments will be multimodels.” Gartner also states “by 2017, all leading operational DBMSs will offer multiple data models, relational and NoSQL, in a single platform.”

Because today’s modern cloud workloads involve numerous components that differ in their data model support requirements, a database that provides multi-model capabilities will deliver a simplified and more agile solution for quickly bringing cloud applications to market. DSE Graph, part of DSE’s multi-model platform, provides support for Cassandra’s key-value and tabular data models, JSON/document models and graph, with each data model inheriting Cassandra’s key benefits and DSE’s enterprise grade functionality.

Availability

DSE Graph will be sold as an option to either a DSE Standard or DSE Max subscription and will be generally available in Q2.

To learn more about DSE Graph, please join us at DataStax Summit Europe taking place April 19-20, 2016 in London.

DSE Graph Resources

About DataStax

DataStax, the leading provider of database software for cloud applications, accelerates the ability of enterprises, government agencies, and systems integrators to power the exploding number of cloud applications that require data distribution across datacenters and clouds, by using our secure, operationally simple platform built on Apache Cassandra™.

With more than 500 customers in over 50 countries, DataStax is the database technology of choice for the world’s most innovative companies, such as Netflix, Safeway, ING, Adobe, Intuit, Target and eBay. Based in Santa Clara, Calif., DataStax is backed by industry-leading investors including Comcast Ventures, Crosslink Capital, Lightspeed Venture Partners, Kleiner Perkins Caufield & Byers, Meritech Capital, Premji Invest and Scale Venture Partners. For more information, visit DataStax.com or follow us @DataStax.

 

 


[1] Gartner, Making Big Data Normal With Graph Analytics, Mark Beyer and Nick Heudecker, July 25 2015
[2] Gartner, Market Guide for NoSQL DBMSs, Nick Heudecker, Merv Adrian, Etisham Zaidi, August 4 2015


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