Big Data Computing Applications - Guide to Cloud Computing for Business and Technology Managers - page 465

21.4.4 Graph Stores or Databases
Social media and the emergence of Facebook, LinkedIn, and Twitter have
accelerated the emergence of the most complex NoSQL database, the graph
database. The graph database is oriented toward modeling and deploying
data that is graphical by construct. For example, to represent a person and
their friends in a social network, we can either write code to convert the
social graph into key-value pairs on a Dynamo or Cassandra or simply con-
vert them into a node-edge model in a graph database, where managing the
relationship representation is much more simplified.
A graph database represents each object as a node and the relationships
as an edge. This means person is a node and household is a node and the
relationship between them is an edge. Like the classic ER model for RDBMS,
we need to create an attribute model for a graph database. We can start by
taking the highest level in a hierarchy as a root node (similar to an entity)
and connect each attribute as its subnode. To represent different levels of the
hierarchy, we can add a subcategory or subreference and create another list
of attributes at that level. This creates a natural traversal model like a tree
traversal, which is similar to traversing a graph. Depending on the cyclic
property of the graph, we can have a balanced or skewed model. Some of the
most evolved graph databases include Neo4J, InfiniteGraph, GraphDB, and
AllegroGraph.
21.4.5 Comparison of NoSQL Databases
1. Column-based databases allow for rapid location and return of data
from one particular attribute. They are potentially very slow with
writing, however, since data may need to be shuffled around to
allow a new data item to be inserted. As a rough guide then, tradi-
tional transactionally oriented databases will probably fair better in
an RDBMS. Column based will probably thrive in areas where speed
of access to nonvolatile data is important, for example, in some deci-
sion support applications. You only need to review marketing mate-
rial from commercial contenders, like Ingres Vectorwise, to see that
business analytics is seen as the key market and speed of data access
the main product differentiator.
2. If you do not need large and complex data structures and can always
access your data using a known key, then key-value stores have a
performance advantage over most RDBMS. Oracle has a feature
within their RDBMS that allows you to define a table at an index-
organized table (IOT), and this works in a similar way. However,
you do still have the overhead of consistency checking, and these
IOTs are often just a small part of a larger schema. RDBMS have a
reputation for poor scaling in distributed systems, and this is where
key-value stores can be a distinct advantage.