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quickly, and analyze it to identify emerging signatures and patterns on the
network packets as they flow across the network infrastructure.
Finally, from a governance perspective, consider the added benefit of a Big
Data analytics velocity engine: If you have a powerful analytics engine that
can apply very complex analytics to data as it flows across the wire, and you
can glean insight from that data without having to store it, you might not
have to subject this data to retention policies, and that can result in huge sav-
ings for your IT department.
Today's CEP solutions are targeted to approximately tens of thousands of
messages/second at best, with seconds-to-minutes latency. Moreover, the
analytics are mostly rules-based and applicable only to traditional data
types (as opposed to the TerraEchos example earlier). Don't get us wrong;
CEP has its place, but it has fundamentally different design points. CEP is a
non-programmer-oriented solution for the application of simple rules to
discrete, “complex” events.
Note that not a lot of people are talking about Big Data velocity, because
there aren't a lot of vendors that can do it, let alone integrate at-rest technolo-
gies with velocity to deliver economies of scale for an enterprise's current
investment. Take a moment to consider the competitive advantage that your
company would have with an in-motion, at-rest Big Data analytics platform,
by looking at Figure 1-1 (the IBM Big Data platform is covered in detail in
Chapter 3).
You can see how Big Data streams into the enterprise; note the point at
which the opportunity cost clock starts ticking on the left. The more time
that passes, the less the potential competitive advantage you have, and the
less return on data (ROD) you're going to experience. We feel this ROD
metric will be one that will dominate the future IT landscape in a Big Data
world: we're used to talking about return on investment (ROI), which
talks about the entire solution investment; however, in a Big Data world,
ROD is a finer granularization that helps fuel future Big Data investments.
Traditionally, we've used at-rest solutions (traditional data warehouses,
Hadoop, graph stores, and so on). The T box on the right in Figure 1-1
represents the analytics that you discover and harvest at rest (in this case,
it's text-based sentiment analysis). Unfortunately, this is where many
