Databases Reference
In-Depth Information
IT operations intelligence became a significant contributing factor to changing the
paradigm: e.g., running through massive system generated logs to understand potential
bottlenecks, alerting the system engineers of an impending system crash, automatically
fixing performance issues in the systems, score cards outlining every system's health, and
most importantly security and threat detection management.
Underlying the IT transformation is big data and analytics. Analyzing massive
amounts of system-generated log data is not a trivial pursuit: these data are cryptic in
nature and get generated every nanosecond.
Figure
3-17
illustrates a conceptual architecture that takes in log data, which is
mostly voluminous and highly unstructured and delivers several analytics services
analyzing the logs and delivering real-time insights.
Figure 3-17.
Big data analytics platform for log analysis
End Points
There's a lot of hype around what a big data analytics platform can deliver, and there is
also a host of industry use cases emerging to prove the point. The hardest part of big data
analytics is finding an appropriate use case. While every industry is poised to leverage
the benefits of big data and analytics, at the same time one needs to understand the fact
that the real value from big data and analytics can be derived only if there is a well-
thought-out business-relevant scenario. There are many instances of over-enthusiastic
technology practitioners chasing elaborate big data technology solutions for a seemingly
less significant business outcome use case.
While big data platforms provide a powerful tool to companies as evident from the
use cases discussed earlier in this chapter, the process of implementing such a platform
and laying down the processes to govern the initiatives is not a trivial pursuit and can
