Today, the world is trying to create and educate data scientists
because of the phenomenon of Big Data. And everyone is looking
deeply into this technology. But no one is looking at the larger
architectural picture of how Big Data needs to fit within the
existing systems (data warehousing systems). Taking a look at the
larger picture into which Big Data fits gives the data scientist
the necessary context for how pieces of the puzzle should fit
together. Most references on Big Data look at only one tiny part of
a much larger whole. Until data gathered can be put into an
existing framework or architecture it can't be used to its full
potential. Data Architecture a Primer for the Data Scientist
addresses the larger architectural picture of how Big Data fits
with the existing information infrastructure, an essential topic
for the data scientist. Drawing upon years of practical experience
and using numerous examples and an easy to understand framework.
W.H. Inmon, and Daniel Linstedt define the importance of data
architecture and how it can be used effectively to harness big data
within existing systems. You'll be able to: Turn textual
information into a form that can be analyzed by standard tools.
Make the connection between analytics and Big Data Understand how
Big Data fits within an existing systems environment Conduct
analytics on repetitive and non-repetitive data
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