Why Graph? A Look at How Cloud Applications Benefit From Graph Technology

The applications that are changing business today are radically different than ones of prior generations.
Today’s applications consist of many endpoints including browsers, mobile devices, and/or machines that
are geographically distributed, intensely transactional, always available, as well as instantaneously and
intelligently responsive no matter the number of users or machines using the application.

Such applications – that DataStax defines as cloud applications – have multi-faceted requirements where
their data management is concerned, with success being defined as the application having:
• Continuous availability
• Geographical data distribution
• Operational low latency
• Linear scalability
• Immediate decisiveness
• Functional cohesiveness
• Operational maturity

To meet these requirements, the application’s data backend includes numerous pieces of DBMS (DataBase
Management System) technology and data-centric engines that must be blended together to act as one –
transactional, analytical, search, in-memory, and others – each of which must run in a highly performant way
without impacting the other’s speed. In other words, the system must be functionally cohesive within a single
architecture and also competently manage the varying workloads.


The data flowing through these systems is very complex, ever changing, large in volume, and highly
connected (i.e. it possesses a very high number of relationships between data elements). Expectations for
the data to be immediately decisive are high, with the need being to instantly answer questions such as:


What products or actions should we recommend to a user based on their preferences and behavioral
patterns to maximize sales or user engagement?


Should an initiated transaction be considered fraudulent or malicious based on past user actions and normal
patterns of system behavior?


These and similar questions cannot be well serviced by legacy database technology because the number of
data relationships coupled with the data distribution, scale, performance, volume, and uptime requirements
of the application are not a fit for a relational database. These requirements, however, are addressed natively
by a graph database that possesses scale-out and active-everywhere capabilities.


This paper provides an introduction to graph databases and discusses when and where they should be used
in today’s cloud applications. It also provides a look at DataStax Enterprise (DSE) and DSE Graph, which is a
scale-out graph database used to manage complex and highly connected data.

 Digital

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