Sept-Oct 2019

We are all aware of the inherent value of customer data within the databases that support our applications. This data identifies customers, accounts, transactional interactions and other patterns that are important for understanding customer behaviors and habits – all of which are useful for improving business processes and customer satisfaction. But what about the value of data about the data processing workloads? Is there value in understanding the processing workloads of our customers? What could we do with workload information if we had the ability to easily organize workload data into meaningful categories and groups, and view the dynamics of the processing patterns over time?

It has been quite a while since I was given the opportunity to spend as much time as I have of late in my office. There is a nice feeling about having an opportunity to follow routines. We all live busy lives and there’s no doubt that having time to kick-around ideas with colleagues represents time well spent, but even so, there really isn’t any substitute for networking with those working with the products you cover in presentations and in articles, commentaries and blog posts. Shortly, the pace picks up considerably as with the arrival of the fall late in September, the number of gatherings of NonStop user groups kicks into high gear.

There has been a lot more interest in virtual NonStop since the support of the VMware Hypervisor. I am aware of many proof of concepts testing virtual NonStop. There still seems to be some confusion around what virtual NonStop means exactly. Many jump to the conclusion that NonStop can now be run in a public cloud such as AWS or Azure. This is not currently possible since, to make virtual NonStop, actually ‘NonStop’ requires some fairly stringent configurations. These things are not readily available to configure in a public cloud. That depends on your definition of cloud; but in general, yes, if you mean a private cloud...

[vc_row][vc_column][vc_empty_space][vc_column_text]As we go into the third decade of the 21st century, we see how much the predictions about data proliferation were mostly true. In the 20th century, we considered terabytes of storage to be large and difficult to manage. Now, it is common for enterprises to manage exabytes – that’s 6 orders of magnitude more than what we considered normal just 15-20 years go.…