Database Reference
In-Depth Information
You might be wondering about the answers to the questions we asked you
to consider at the start of this section; our answer is likely to match yours at
this point—
faster
and
easier
. Solutions that utilize large data sets are often
more helpful than those constrained to smaller data sets.
Big Data Use Cases:
Patterns for Big Data Deployment
We debated over what use cases to cover in this chapter; we started by trying
to figure out what industries don't have use case fits for Big Data. We thought
we had a winner when someone blurted out “the Disc Jockey industry,” but
then we had one student on BigDataUniversity.com pull his entire music col-
lection into BigInsights and subsequently build some pretty cool analytics on
his music files, including a musical genomics-oriented application. So much
for that approach to narrowing the field—this led us to select use cases that
help tap patterns that appear to be common and cross-industry. For example,
reaching for new kinds of data and applying analytics as the data streams off
the collection units rigged with sensors can be used to gain better insight into
traffic systems, networks, the health of a newborn baby, loan topic valua-
tions, and more.
The use patterns detailed here involve BigInsights, IBM InfoSphere Streams
(Streams), and a combination of new age Big Data engines with traditional
warehousing, such as the IBM PureData System for Analytics (formerly
known as Netezza) or the IBM PureData System for Operational Analytics
platforms. There's a common trend across all of the cases that we cover: they
all involve a new way of doing things that's more practical (and now finally
possible) with a Big Data platform.
You Spent the Money to
Instrument It—Now Exploit It!
In Chapter 1 we gave you all kinds of metrics and examples that illustrate
just how instrumented our world is: from bridges, to rail tracks, livestock,
conference badges, running shoes, and more, the world can collect some
kind of data from just about anything, anywhere, at any time.
