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Mainframe Modernization Starts with Data

Gil Peleg


Aug 4, 2021

Mainframe modernization is a top priority for forward-thinking I&O leaders who don’t want to be left behind by rapid technological change. But moving applications to the cloud has proven to be difficult and risky, which can lead to analysis-paralysis or initiatives that run over budget and fail. This is slowing the pace of adaptation, starving cloud functions of access to mainframe data, and often inhibiting any positive action at all.

So, at the moment, most mainframe data is effectively siloed, cut off from BI or AI cloud applications and data lakes in the cloud and simply locked in a “keep the lights on” mentality that is dangerous if continued too long.

Part of the problem is that mainframe organizations have focused on an application-first approach to cloud engagement which is usually the wrong approach because cost and complexity get in the way. Leaders should instead take a data-first approach that allows them to begin modernization by liberating mainframe data and moving storage and even their backup and recovery functions to the cloud. This has the benefit of making mainframe data immediately accessible in the cloud without requiring any mainframe application changes.

Why Is Mainframe Modernization So Hard?

The mainframe environment has been aptly described as a walled garden. Everything inside that garden works well, but the wall makes this Eden increasingly irrelevant to the rest of the world. And the isolation keeps the garden from reaching its full potential. 

The walled garden is a result of the inherent nature of mainframe technology, which has evolved apart from the cloud and other on-prem environments. This means the architecture is fundamentally different, making a so-called lift-and-shift move to the cloud very difficult. Applications built for mainframe must stay on the mainframe and adapting them to other environments is often prohibitive. At an even more fundamental level, mainframe data is stored in forms that are incomprehensible to other environments.

How does Model9 Cloud Data Manager for Mainframe Work?

While mainframe lift-and-shift strategies may be very challenging, the movement of data to the cloud has suddenly gotten much easier thanks to Model9 Cloud Data Manager for Mainframe, which represents a fresh technology direction.

Our patented and proven technology takes mainframe data and moves it quickly and easily to the cloud and can then transform it in the cloud to almost any industry-standard form. 

With Model9, mainframe data is first moved to an object storage target in a public cloud or a private cloud . The process is vendor agnostic and eliminates most of the traditional costs associated with mainframe ETL because it leverages zIIP engines to handle movement (over TCP/IP) and accomplishes the “transform” step in the cloud, without incurring MSU costs.

This can work with any mainframe data but is especially helpful for historic data and any data resident on tape or virtual tape, normally hard to access even for the mainframe itself.

The result is backup, archiving, and recovery options in the cloud that are cost-effective, faster, and easier to access than in traditional on-prem systems. And, Model9 has almost no impact on existing mainframe applications and operations. It is a data-first approach that allows you to transition mainframe data into the cloud with a software only solution

The Benefits Of Model9’s Data-first Approach

By focusing on the simpler task of moving mainframe data first, organizations gain multiple advantages including:

  • Cost Reduction by reducing or eliminating the tape or VTL hardware footprint and associated mainframe software (DFSMShsm etc.), as well as reducing MSU charges
  • Cloud can deliver a full data protection solution that can provide security and, “recover anywhere” capability.
  • Cloud-based transformation immediately unlocks mainframe data for use in cloud applications.
  • Cloud can also yield performance improvements such as reduced backup windows, reduced peak processing demand, and reduced system overhead.

Data First Works

Data-first mainframe modernization empowers leaders to broaden their cloud adoption strategy and secure more immediate benefits. It can accelerate cloud migration projects by leveraging non-mainframe skills and delivering simplified data movement. Organizations can readily maintain and access mainframe data after migrating to the cloud to meet corporate and regulatory requirements without the traditional high costs.

In addition, a data-first approach reduces the burden on your mainframe by offloading data to external platforms while empowering analytics and new applications in the cloud for some of your most valuable data.

According to Gartner, with new approaches to data and cloud, ‘Through 2024, 40% of enterprises will have reduced their storage and data protection costs by over 70% by implementing new storage technologies and deployment methods.’

Best of all, a data-driven approach allows organizations to combine the power of the mainframe with the scalability of the cloud and modernize on their own terms.

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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