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Getting Started with Greenplum for Big Data Analytics PDF 下载
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Data formats generated and consumed may not be structured (for example,
relational data that can be normalized). This data is generated by large/
small scientific instruments, social networking sites, and so on. This can be
streaming data that is heterogeneous in nature and can be noisy (for example,
videos, mails, tweets, and so on). These formats are not supported by any of
the traditional datamarts, data store/data mining applications today.
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
advanced 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
framework like MapReduce has attempted to address this need.
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
procedures that fosters discovery with data at its center. This discovery process
refers to our ability to derive business value from data and includes collecting,
manipulating, analyzing, and managing data.
We are talking about four discrete properties of data that require special tools,
processes, and procedures to handle:
• Increased volumes (to the degree of petabytes, and so on)
• Increased availability/accessibility of data (more real time)
• Increased formats (different types of data)
• Increased messiness (noisy)
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