Database Reference
In-Depth Information
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Utilization of compute intensive NLP (natural language processing)
and Machine Learning techniques.
Of course the work done on this use case led us to a set of challenges that
seem to show up every time we end up recommending a Big Data solution,
specifically:
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Big Data solutions are ideal for analyzing not just raw structured data,
but also semistructured and unstructured data from a wide variety of
sources—our insights came from the intersection of all of these sources.
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Big Data solutions are ideal when you're not content with the
effectiveness of your algorithms or models; when all, or most, of the
data needs to be analyzed; or when a sampling of the data isn't going
to be nearly as effective. They are also helpful when you're not content
to just sample the data—you want to look at every interaction because
you believe it will yield a competitive advantage (it's that whole
population analytics theme we talked about in Chapter 1).
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When business measures on the data are not predetermined, Big Data
solutions are ideal for iterative and exploratory analysis—we
deliberately took an iterative/agile approach to learning.
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Big Data solutions are ideal when you aren't completely sure where
the investigation will take you, and you want elasticity of compute,
storage, and the types of analytics that will be pursued—all of these
became useful as we added more sources and new methods.
What's important, really important, to notice here is that we augmented
the power of the existing analytics investment. It's not appropriate to rip and
replace in most cases; rather, clients we work with want to increase the yields
of their current investments. With this client, we used a Big Data solution to
leverage data that was not suitable for their traditional warehouse environ-
ment along with what we knew from the warehouse.
Big Data technologies are also well suited for solving information chal-
lenges that don't natively fit within a traditional relational database approach.
It's important to understand that conventional database technologies
are
key and relevant, and part of an overall analytic solution. In fact, they become
even more vital when used in conjunction with your Big Data platform. We
always say: “Big Data isn't just Hadoop.” With that said, some problems
