Big Data, Analytics, and Data Science Life Cycle - Getting Started with Greenplum for Big Data Analytics - page 7

videos, mails, tweets, and so on). These formats are not supported by any of
the traditional datamarts, data store/data mining applications today.
Note
Noisy data refers to the reduced degree of relevance of data in context.
It is the meaningless data that just adds to the need for higher storage
space and can adversely affect the result of data analysis. More noise in
data could mean more unnecessary/redundant/un-interpretable data.
• Traditionally, business/enterprise data used to be consumed in batches, in
specific windows and subject to processing. With the recent innovation in ad-
vanced devices and the invasion of interconnect, data is now available in real
time and the need for processing insights in real time has become a prime
expectation.
• With all the above comes a need for processing efficiency. The processing
windows are getting shorter than ever. A simple parallel processing frame-
work like MapReduce has attempted to address this need.
Note
In Big Data, handling volumes isn't a critical problem to solve; it is the
complexity involved in dealing with heterogeneous data that includes a
high degree of noise.
So, what is Big Data?
With all that we tried understanding previously; let's now define Big Data.
Big Data can be defined as an environment comprising of tools, processes, and pro-
cedures that fosters discovery with data at its center. This discovery process refers
to our ability to derive business value from data and includes collecting, manipulat-
ing, analyzing, and managing data.