Big Data Computing Applications - Guide to Cloud Computing for Business and Technology Managers - page 451

reducing the overall database footprint, while dramatically improving query
performance. The simplicity of the columnar approach provides many ben-
efits, especially for those seeking a high-performance environment to meet
the growing needs of extremely large analytic data sets.
21.2.3 NoSQL Data Management
NoSQL suggests environments that combine traditional SQL (or SQL-like
query languages) with alternative means of querying and access. NoSQL
data systems hold out the promise of greater flexibility in database manage-
ment while reducing the dependence on more formal database administra-
tion. NoSQL databases have more relaxed modeling constraints, which may
benefit both the application developer and the end-user analysts when their
interactive analyses are not throttled by the need to cast each query in terms
of a relational table-based environment.
Different NoSQL frameworks are optimized for different types of analyses.
For example, some are implemented as key-value stores, which nicely align
to certain big data programming models, while another emerging model is a
graph database, in which a graph abstraction is implemented to embed both
semantics and connectivity within its structure. In fact, the general concepts
for NoSQL include schema-less modeling in which the semantics of the data
are embedded within a flexible connectivity and storage model; this pro-
vides for automatic distribution of data and elasticity with respect to the use
of computing, storage, and network bandwidth in ways that don't force spe-
cific binding of data to be persistently stored in particular physical locations.
NoSQL databases also provide for integrated data caching that helps reduce
data access latency and speed performance.
A relatively simple type of NoSQL data store is a key-value store, a schema-
less model in which distinct character strings called keys are associated with
values (or sets of values, or even more complex entity objects)—not unlike
hash table data structure. If you want to associate multiple values with a
single key, you need to consider the representations of the objects and how
they are associated with the key. For example, you may want to associate a
list of attributes with a single key, which may suggest that the value stored
with the key is yet another key-value store object itself.
The key-value store does not impose any constraints about data
typing or data structure—the value associated with the key is
the value, and it is up to the consuming business applications to
assert expectations about the data values and their semantics
and interpretation. This demonstrates the schema-less property of the
model.