any number of documents of any type. This is the key feature of
document oriented databases to become schema-less as well as
giving them the advantage of porting and storing data of different
types easily.
Document databases focus on storage and access optimization
for documents as opposed to rows or records. The document
database entities are just records with multiple fields. All of
the current document databases support documents in JSON
(JavaScript Object Notation) format.
Some document databases are BigTable databases, others provide
MapReduce processing, while some of them are columnar
databases. The inference is that the storage and access implementation
is independent of the database’s orientation towards
document-oriented databases. Some document databases provide
added capabilities such as SQL or SQL-like query processing capabilities.
For instance, Terrastore document store provides advanced
scalability and elasticity features without sacrificing Consistency.
(e) Graph Databases
Graph Databases (GD) store information in multi-attribute tuples
that reflect relationships in a different way. At the top of GD
there may be a key/value store, columnar database, ‘‘BigTable’’
database or combination of these and other architectures.
A graph database might be used to store the ‘‘friend’’ relationships
of a social network, with a record merely consisting of
two friends who share a relationship. GDs are suitable for handling
highly interconnected data and therefore are very efficient
in traversing relationships between different entities. In Facebook
everything is a Graph.
The features of GD are:
• ACID compliant.
• Inspired by Euler’s Graph theory.
• Date model: Nodes, relations, K-V on both.
Graph Databases are a suitable for the following types of
applications [3]:
• Location-based Services.
• Recommendation Engines.
• Complex network-based applications such as Social, Information,
Technological, and Biological networks.
Table 3 summarizes the salient features of various NoSQL
models discussed above.