Big Data Computing Applications - Guide to Cloud Computing for Business and Technology Managers - page 447

A processing cluster configured for big data computing is typically
able to continue operation with a reduced number of nodes fol-
lowing a node failure with automatic and transparent recovery of
incomplete processing.
A final important characteristic of big data computing systems is the inher-
ent scalability of the underlying hardware and software architecture. Big
data computing systems can typically be scaled in a linear fashion to accom-
modate virtually any amount of data or to meet time-critical performance
requirements by simply adding additional processing nodes to a system con-
figuration in order to achieve billions of records per second processing rates
(BORPS). The number of nodes and processing tasks assigned for a specific
application can be variable or fixed depending on the hardware, software,
communications, and distributed file system architecture. This scalability
allows computing problems once considered to be intractable due to the
amount of data required or amount of processing time required to now be
feasible and affords opportunities for new breakthroughs in data analysis
and information processing.
One of the key characteristics of the cloud is elastic scalability:
users can add or subtract resources in almost real time based on
changing requirements. The cloud plays an important role
within the big data world. Dramatic changes happen when
these infrastructure components are combined with the advances in
data management. Horizontally expandable and optimized infrastruc-
ture supports the practical implementation of big data. Cloudware
technologies like virtualization increases the efficiency of the cloud
that makes many complex systems easier to optimize. As a result,
organizations have the performance and optimization to be able to
access data that were previously either unavailable or very hard to
collect. Big data platforms are increasingly used as sources of enor-
mous amounts of data about customer preferences, sentiment, and
behaviors. Companies can integrate this information with internal
sales and product data to gain insight into customer preferences to
make more targeted and personalized offers.
21.1.3 Big Data Appliances
Big data analytics applications combine the means for developing and imple-
menting algorithms that must access, consume, and manage data. In essence,
the framework relies on a technology ecosystem of components that must be
combined in a variety of ways to address each application's requirements,
which can range from general information technology (IT) performance
scalability to detailed performance improvement objectives associated with