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tremendous problem of latency. Further, the methods used to transform the data can often lead
to inconsistency, something that dramatically reduces the efficacy of BI tools and analytics tools.
It’s clear that ETL doesn’t cut it in today’s business environment. Mainframe data virtualization is
the future. The software required to do this runs directly on a mainframe, but not through its
central processors. Rather, the software is run concurrently on specialized processors, like the
IBM System Z specialty processor. This architecture obviates problems with software license
charges, as well as problems with MIPs capacity. Overall, the TCO of a mainframe is reduced,
and the production of data can continue on the mainframe unabated.
Mainframe data virtualization is the future, as it’s capable of bring analytics and mainframe data
as close together as they can possibly be. Latency is no longer an issue with this method, and
consistency is strictly enforced on the data. Further, non-relational data can be accessed easily
through SQL queries, and the data, overall, can be easily dealt with through Business
Intelligence tools and analytics tools. Finally, there is no need for developers to familiarize
themselves with any specific mainframe, because the virtualization software takes care of that.
This method of dealing with mainframe data allows business decision makers to do what they
do best: grow their businesses and mitigate the risk facing their businesses. It allows for
relationships to be developed between the systems, processes, and people that make up a
business, by allowing vital information to be accessed in real time. The end result is that
businesses employing mainframe data virtualization are better able to identify impending
threats, maximize new opportunities, and effectively manage the expectations of their
customers.
About the Author
Mike Miranda is a writer and PR person for Rocket Software.
48 Cyber Warnings E-Magazine – March 2015 Edition
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