One of the things that has often driven me nuts with the rise of cloud computing is the assumption that the demise of the mainframe is a foregone conclusion. I’ve often written about the reality that not all applications and data sets belong on the cloud, especially ones that reside on mainframes.

Although I have often been laughed out of a meeting for this opinion, adoption patterns have proved me correct. It’s often spun as a pushback on cloud computing itself, but it’s just pragmatism.

The truth is that we’re going to find applications and data sets (more than you think), that are not viable to move to the cloud. I call this the point of saturation, when we’re done moving most of the applications to the cloud that are practical to relocate. I’ve had mainframe applications in mind when saying that, and for good reason.

It can be done

Those who push back on this assertion quickly point out that some tools and technologies allow older mainframe applications to be ported to a cloud provider. These solid products set up emulators and code converters to run mainframe-based applications on public cloud providers.

Yes, you can go this route. But should you? The answer is often no. Consider the additional cost, risk, and the reality that these applications will have value for much longer than many predicted.

A survey from application modernization firm Advanced found that digital transformation is typically a priority. However, that often does not mean the end for the mainframe. Indeed, only 6% of respondents believed alternative technologies would replace the mainframe shortly.

More than half of the companies (52%) plan to maintain or grow their dependency on mainframes. Moreover, half of respondents said mainframes are their preferred platform for core applications (56%).

Coexistence is key

I’ve always seen an integration and coexistence policy as a better path, depending on how an enterprise uses the mainframe. Even with the explosive interest in generative AI where the mainframe won’t be a preferred platform (see the same report), mainframes become a primary server of training data. They sometimes have historical data that goes back 50 years. That’s invaluable for building large language models, which should learn from old as well as new data.

I don’t mean that we should prefer a mainframe platform over any other platform; it should be fairly considered—as all platform options should be. Sometimes the cloud will be a better host, sometimes edge computing, and in some cases, mainframes will continue to provide value. This “it depends” response drives everyone nuts, but it is usually the correct answer to these problems.

This approach creates a digital ecosystem of many different platforms, all of which are the best platforms for the specific use cases. Thus, we also need to improve at managing complexity and heterogeneity, which enterprises are not excelling at today. Enterprises are unable to find value in cloud deployment due to too much operational complexity and no finops oversight. Whether you use mainframes or not, you need to address that problem.

I’m not advocating for mainframe platforms. They come with issues, including the big one that mainframe developers and operators are retiring, and there is a shortage of mainframe talent. Many younger IT pros are not attracted to the mainframe space due to its lack of “coolness.”

However, those who understand mainframes and cloud-based platforms are in high demand, typically commanding salaries 20%-30% above their peers. Even cloud architects who know how to interact with mainframes are often paid a premium. Are you noticing a pattern?

I’m a pragmatist. We will use the platforms that can return the most value to the business. I don’t care what that is if it is the most optimized solution. That should be an architect’s main objective.

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