In the spirit of Halloween, I call to mind the words of the horror movie icon Freddy Krueger, who asks of his nemesis in Freddy vs. Jason, "Why won't you die?" That's the same question data old-timers ask about the mainframe -- a haunting IT specter, indeed.
Freddy Krueger
We've been hearing about the mainframe's impending demise since the advent of microcomputers in the 1990s. Over the last 20 years, many organizations have downsized their legacy applications and production systems to prepare for the day when these artifacts from an era of lesser technical intelligence disappeared from the computing landscape.
But, just like Jason, the mainframe lives on… and on. In fact, fresh models keep rolling off the assembly line, as the New York Times reported in August about IBM's new zEnterprise EC12. Microcomputers may have facilitated the personalization of information processing, but the mainframe remains a viable option (though a pricey one) for organizations processing huge volumes of information. Mainframes have been a staple in banking and telecommunications, and they're selling well in emerging markets like Asia and Africa, the Times said.
Now big-data has some organizations revisiting mainframe costs and benefits. If big-data benefits are large enough, they may be able to justify the capital investment in a mainframe. And, given this potential, analysts ought to be aware of the mainframe's big-data processing potential.
For those 40 and younger who have not worked in a mainframe environment, that means becoming familiar with this technology. Your big-data analytics may very well run on a mainframe one day. You should learn the operating system syntax and utilities, which are different from those found in Microsoft Windows or Unix systems. You should become familiar with the storage structure and file naming convention. Furthermore, you should become comfortable with workspace allocation and disk-space partitioning. If your organization adopts a mainframe solution to address big-data resources, you'll benefit by understanding the seasoned methods and tools you'll need to do your work.
If you are older than 40, then you may need to brush off your REXX, CICS, and COBOL skills, so you can create new applications using old tools. Or you'll have to learn how to tie analytics into cloud processing and to tailor legacy code to work on mainframes that may also use blade servers. In effect, the old big iron machines have been reengineered to do some new stuff.
Whether you are young or young at heart, the mainframe is a platform that will become or remain a part of your career. It's been said, "A bend in the road is not the end of the road, unless you fail to make the turn." The folks at IBM have made the turns that prevented the mainframe from reaching the end of the road. Similarly, analysts will have to extend their platform awareness.
Like Jason, the mainframe just won't die. Do you think that's an IT trick or a treat?
@callmebob, the art of the abacus... i can see this is no kindergarten learning tool! I'm sure it's amazing to watch your wife or other abacus experts in action. I'd be lost, no doubt.
My wife is Japanese and she grew up using a soroban and she was speed lightening. You can still find many older mom and pop shops and restaurants using them to calculate their customer's bill. Regarding the profile for a skilled abacus user, they should have nimble fingers, a quick brain, and an underlying comprehension of math and numbers. I found this simple virtual soroban that should help anyone's Sudoku skills.
Beth, - Thanks for the HPA link. Talking of years back, its actually 14 and not 12 now that i think of it. When i had just joined high school...lol. Whats funny is that upto today i still haven't seen an actual mainframe computer though i've visited a handful of relatively big server rooms locally. I should make it a mission to find out who has a mainframe nearby so that i go and look.
In-memory computing can indeed speed up data analysis and help companies gain competitive advantages. In-memory computing benefit from the constantly falling price of memory which most organizations can easily afford nowadays. The move to in-memory computing might even become inevitable for certain analytics solutions where entier set of data need to be investigated rather than a representative.
kichecko -- I remember years ago (more than 12 but I won't say how many!) as a young reporter covering the rapidly changing telecom industry, I had the opportunity to visit an MCI (now long defunct) data center to see its mainframe operations. I knew nothing about computing, but the size was impres sive. Now the smaller the size the more impressive, it seems! I suppose mainframes are more common in US, for example, with more mature markets?
As to HPA -- that's high-performance analytics (analytics run on high-performance computing platforms). SAS, this site's sponsor, has lots of info on HPA to share. If you're interested, you can find out more on its HPA microsite.
I really admire the managers who oversee the IBM mainframe division. Those folks have not only technical skills but also exceptional political skills. They did not get comfortable with their success but saw the advent of the PC and figured out how to protect its product line. They have set a marvelous example for all of the young turks who think that product longevity rolls in on the wheels of inevitability. Lotus 123, Visi-Calc, Word Perfect - a lot of products had their 15 minutes of fame. But the mainframers dug in and made survival a part of the product line. Yes, I was one of those who waited for the demise of the mainframe. But those folks fought for their job security and kept themselves relevant and are still looking for ways to stay relevant. That requires technical and political know-how. They serve as a model for the young product makers of today - find ways of keeping your product relevant - know your business environment - do the R&D - understand what people need - look for ways to integrate your product with other products - stay hungry. This reminds me of an old saying 'In the jungle, whether you are an antelope or a panther, if you stop running, then you will not survive'. The IBM mainframe crew have never stopped running - mega kudos to them!
HPA - High Performance Analytics - using speedy processing techniques like in-database computing, in-memory computing, or grid computing. Basically, you make the analytics faster by moving the processing to another location or splitting up the processing across processing nodes.
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
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