Analyzing mainframe logs is not new but doing it simply is and that’s where the ZETALY difference lies.
Historically, collecting and analyzing mainframe data is far too cumbersome, time consuming, and, frankly, expensive to be realistic for most IT teams. Due to the sheer volume of logs produced by a mainframe, there is no realistic way to leverage the boundless information created by a z/OS environment. Given these obstacles, mainframe data is rarely leveraged to its greatest potential and, as a result, most IT departments miss out on opportunities to make their mainframe cheaper and more productive.
It’s time to do things a little differently.
ZETALY is the mainframe analytics platform made with z/OS optimization in mind. Accessible, automated, and understandable — ZETALY offers the features mainframe managers need to finally make sense of data analytics and apply these insights toward implementing new optimization opportunities.
ZETALY Difference #1 – All mainframe assets in one place
In the past, no one analytics platform could accomplish all of the tasks needed to understand mainframe logs. You would need one to collect, another to analyze, a third to visualize the data, and a fourth machine learning product — fragmented across several platforms, it becomes difficult to correlate information and share insights among the team.
ZETALY Difference #2 – Everything is automated
By default, the mainframe has to be programmed to batch collect and all data-collection activities have to be explicitly dictated to the machine. For a long time this has been a major issue for the integration of data analytics — the effort required to manually input collection and analysis instructions is usually too severe of a pain point for IT departments to overcome. Especially for teams operating with limited resources.
Difference #3 – Manage a huge quantity of data
It’s standard for a mainframe to produce billions of logs a day. Not to mention, this volume is multiplied by the number of mainframes that are being managed. Every interaction that takes place within the system is documented and can be reflected within logs — for most, that is simply too much data to handle. Combing through endless data, and hunting for any actionable insight, can be overwhelming for even the most seasoned IT professional. For most, storage limitations and time constraints are too burdensome to ever bother with collecting or analyzing mainframe data.
Difference #4 – Adaptable user interface
So you’ve got all this mainframe data collected — now what? All that data isn’t worth much if there isn’t an intuitive way to visualize and understand it from a business perspective. Mainframe logs come in without any context and are comprised solely of confounding figures. You’re left to do the analytical heavy lifting, and it can be nearly impossible to extrapolate any meaningful insights from raw numbers.
Difference #5 – Machine learning
The function of machine learning in the context of mainframe management is to help collect and analyze data. It automates performance insights that would have otherwise gone unnoticed. A machine-learning algorithm observes data and teaches itself to make decisions or predictions without explicitly being programmed to do so, and uses this insight to perform tasks, offer insights, and optimize functions.
Machine learning has become standard practice for analyzing servers, but has yet to cross over to mainframes. Due to the extreme volume of logs generated by mainframes each day, average IT teams find machine learning is too complex to integrate.