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?
Actually, I see organizations making decisions to move away from mainframes for cost reasons and stay away from/move cautiously toward HPA for cost reasons. There are exceptions like the Census Bureau, which processes huge volumes of data and has licensed most of SAS' products. But outside of the Census Bureau, shops with legacy applications are trying to migrate productions systems to UNIX servers to save money. Often these same shops have invested in contract labor augmentation vis-à-vis investing in new analytic software such as HPA. Seasoned contractors with database and analytic skills can mimic the HPA environment without the sponsors having to purchase additional software. Location is also important. SAS human capital is abundant between Virginia (Northern) to New Jersey; NESUG is one of the largest conferences. In those areas where talent acquisition is a challenge, organizations lean toward purchasing solutions like HPA. In short, companies invest in either people or a product line. Acquiring affordable off-shore talent is also a popular option in the effort to migrate from big boxes and high-end solutions. Traditionally, SAS' did most of its high end solutions in Europe, the Middle East and Asia because the talent pool was sparse. The US had more analytics talent (as demonstrated by the plethora of users groups). If shops can get their staffs to design efficient solutions, they will do so (the cost of training is less than expanding the range of licensed products). But if the younger talent pool has more heterogeneous interests (products beyond analytics), the HPA is seen as the better choice.
Some things never die. Everyone still has a calculator lying around the house, heck I even have a TI 10-key adding machine I could never part with, particularly helpful during tax season. I'm not surprised that the rust has been scraped off the Big Iron with the rise of Big-Data and the demand for number crunching is heightened. Now, if the power grid can keep pace.
So, what's next? Time to dust off my abacus skills?
For those of us who are old school but young at heart, we will concede that the mainframe won't die. It just keeps getting reinvented. At the IACIS conference in Myrtle Beach earlier this month, network appliances were demonstrated that enable computer labs or workstation clusters to be set up using virtualization. Effectively, what was being shown was a new-fangled, Windows-based version of mainframe/terminal architecture. It was presented as if it were something new, and yet the underlying concepts were exactly the same as whay my dad was doing at Sperry/UNIVAC 40 years ago! At that same conference, cloud concepts were demonstrated, but you could have pulled the word "cloud" out of the paper and replaced it with "timesharing" and the paper could have just as easily have been written about MIT's 1960's project CTSS.
In my view, there are certain foundational concepts or architectures, if you will, that transcend the passage of time. The hardware will get faster, more sophisticated, more capable, but the underlying implementation concepts remain largely unchanged. Certainly there were game-changing technologies that invent their own framework or foundation, but those are few and far between.
That said, there's no shame in building a better mainframe, even if it's not sexy to call it that.
When i began learning about computers something like 12 years ago, we already learnt about mainframes in the history part of computers and didn't have a place we could go to see an actual mainframe. Its interesting how they've been creeping back to relevance in the wake of big-data since about 2 years ago, something like the return of the dinosaurs and the IBM empire...
Hi Bryan, we hear a lot these days about high-performance analytics, using speedy processing techniques like in-database computing, in-memory computing, or grid computing (HPA, of course, is favorite of SAS, this site's sponsor). Do you see organizations making a mainframe or HPA decision?
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