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Top Trends in Cloud Data Management for Mainframe in 2021

Gil Peleg


Jan 5, 2021

At Model9, our position in the industry gives us a unique vantage point on what is happening across both the mainframe and cloud world. Clearly, while everyone expects the movement to the cloud to continue, with Gartner even going so far as to suggest that around one-third of mainframe storage will be in the cloud by 2025, there are some glitches and trends that we find interesting.

The unifying theme is the need to do more with data and do it at the lowest cost possible.  Cloud costs are often extremely low in comparison with traditional on-premises costs, while also offering innumerable advantages in terms of data mobility and cost-effective analytics. The big challenge is data movement “up” from on-premises repositories to those cloud opportunities. 

Rediscovering Mainframe Value

Looking ahead, it seems that mainframe organizations are finally realizing that successful digital transformation requires adopting modern technology solutions, taking some risks, and not just relying on professional services firms to force feed the process. That means these same organizations will be looking to optimize their investment in MF instead of simply migrating off the platform.  They need a way to optimize the value of their investments by integrating mainframe and cloud rather than attempting the risky path of migrating completely away from the mainframe. Moving data and enhancing analytics is a great place to start.  The goal will be first, to seek to leverage cloud analytics and BI tools for their MF data and, second, to leverage cloud technologies to simplify daily processes and reduce IT costs. 

Speeding Past ETL

Last year’s BMC survey discussed the continued support for mainframes even as cloud storage continues to grow…We have heard tales of woe from individuals at some well-known independent companies that were built around the expectation of a substantial and rapid mainframe-to-cloud transition. The problem they face is that traditional data movement (extract, transform, load –ETL) processes are slow and expensive (by comparison with newer extract, load and transform – ELT), contributing to slower than expected movement to the cloud, and perhaps even driving some fear, uncertainty, and doubt amongst mainframe operators about the path ahead.  With Gartner pointing to the compelling case for cloud storage, we expect more mainframe shops to look beyond the “same old” movement strategies in the year ahead to try something new.  Certainly, there is no longer any question about the capabilities of these options — and again, Gartner has pointed this out in their report.

A Superior Data Lake 

Another thing we definitely see from customers is a move to create and/or grow bigger and better data lakes.  Data lakes are almost always synonymous with cloud storage.  The economics are compelling and the analytic options, equally appealing.

Analysts are predicting as much as a 29 percent compound annual growth rate (CAGR) for data lake implementation over the next five years.  We also see that organizations want all their data in the data lake, so they can run comprehensive analytics, with AI, ML and every other tool of the trade. That means, if at all possible they want to include mainframe data, which is often critical for understanding customers and the business as a whole. And that means wrestling with the challenges of data movement in a more effective way than in the past. It is almost like getting organizations to make the leap from “sneaker net” (the old days when companies transferred big files by moving floppy disks or portable drives) to actual high-bandwidth networks.  The leap in data movement and transformation today is equally dramatic.

The Competitive Edge Provided by Data

As more and more companies do succeed in sharing their mainframe data within a data lake, the superiority of outcomes in terms of analytic insight is likely to create a real competitive advantage that will force other companies to take the same path. It’s a sort of corollary to the data gravity theory. In this case, when others move their data, you may be forced by competitive pressure to do the same thing. 

Without that critical spectrum of mainframe information, a data lake can easily become what some are terming a Data Swamp — something that consumes resources but provides little real value.  In the year ahead, this view of data gravity should resonate more with decision-makers and will start to inform their strategic decisions.

Multicloud Becomes Mainstream  

In the early days of cloud, as competition began to ramp up and as more and more companies migrated work to the cloud, the use of multi-cloud became controversial. Pundits worried on the one hand about vendor lock-in and on the other about complexity. It turns out, customers tell us they often need multicloud.  Just as on-premises operations often depend on multiple hardware and software vendors, different clouds have different strengths and customers seem to be simply picking the best-of-breed for a specific purpose. Fortunately, data movement between mainframe and cloud is getting easier!

That leads us to think multicloud will no longer be treated as exceptional or controversial and instead, organizations will focus on making the most of it.

No matter how the Covid crisis and the global economy evolve over the next year, the cloud-mainframe relationships will be dynamic – and interesting to watch. The “new oil” of data will continue to influence us all and will put a premium on getting storage out of static storage and into circulation where it can be monetized.  We wish our customers the best as they navigate these waters!

About the author

Gil Peleg | CEO
Gil has over two decades of hands-on experience in mainframe system programming and data management, as well as a deep understanding of methods of operation, components, and diagnostic tools. Gil previously worked at IBM in the US and in Israel in mainframe storage development and data management practices as well as at Infinidat and XIV. He is the co-author of eight IBM Redbooks on z/OS implementation.
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