Modernizing mainframe technology has become a top priority for many organizations. Yet, many organizations need help figuring out where to start. There is no one-size-fits-all approach to upgrading your mainframe. Therefore, understanding your business and what it needs is essential before you can begin modernizing mainframes.
Mainframe modernization applications don’t mean ripping out legacy systems and replacing them with the latest and greatest technology. Instead, it would help to understand how your mainframe applications work, what data they are using, and how to use that data in a new way.
Why Mainframe Modernization Is Hard
The mainframe computer was designed to handle one task at a time. The primary advantage of the mainframe was its ability to execute a command in a brief period. This allowed it to take transactions at high speed, which made it ideal for banking, airline reservation systems, and other transaction-intensive industries. The mainframe’s strength was also its weakness: It could only process one task simultaneously. It cannot be reprogrammed as quickly as an off-the-shelf PC or server and can’t take advantage of the advances in computer hardware that have been made over the past few decades.
The data-first approach of mainframe modernization aims at starting with the data. If your business runs on mainframes, it’s because you have a lot of transactional workloads that must be processed daily. These transactional workloads are easy to understand and implement — they involve writing code and running queries against your database. What about other kinds of work, analytics, and machine learning? These aren’t things you can run on your mainframe without massive adaptations. It would help if you had something else: an engine that can process these types of workloads at scale.
The Data-First Approach to Modernizing Mainframes
The data-first approach to modernizing mainframes is a new way of thinking about the technology that underpins businesses. It’s a mindset that focuses on data and its value to an organization rather than the hardware it runs on or the software that makes it work. If your mainframe can’t keep up with the demands placed upon it by modern applications, then you need to do something about it. There are plenty of ways to do so:
- Change your programming language – You can change the programming language used by your mainframe from COBOL to Java or C++ and get similar results in less time and with fewer resources. You can use Python (or another programming language).
- Use a virtualization platform – Virtualization platforms allow you to run multiple operating systems on one physical machine. You can buy new hardware or move all your existing applications over at a time. You could also use this method for easier upgrades in the future because you won’t have to worry about upgrading all those different operating systems separately anymore.
Example of the Data-First Approach to Modernizing Mainframes
An example of a data-first approach to modernizing mainframes is creating APIs for the mainframes. Use API management to help create a framework for letting other apps access your mainframe data. API management creates a controlled environment for developers to consume APIs. Investigate API management tools that provide analytics and monitoring. API analytics can help you understand what data is being consumed and help determine trends in API usage. Monitoring allows you to monitor the resources being consumed by APIs to ensure your system remains stable.
API management helps you create an environment for developers that makes it easy for them to consume your mainframe APIs. It also allows them to use those same APIs with minimal overhead or restrictions on what they can do with them—allowing for maximum flexibility in how they work with your data. Since this process is automated, there’s no need for manual input from administrators during setup or maintenance; when new apps want to access, they submit an application form through a portal or self-service portal.
Benefits of the Data-First Approach
The data-first approach is a new way of thinking about the mainframe. It’s a way to modernize and transform your mainframe application into a flexible, resilient system that can be easily updated and expanded as your business grows.
What does this mean for you? Here are some of the benefits:
- Better performance. The mainframe is designed around batch processing and large volumes of data, which means it will never be able to keep up with today’s demands for real-time analytics, IoT connectivity, and machine learning. The data-first approach allows you to modernize your mainframe application to take advantage of these features to improve overall performance and enable more users at once.
- Increased agility. The data-first approach allows you to scale your mainframe application without worrying about upgrading hardware or hiring additional staff members. You can add more nodes or upgrade your hardware without worrying about affecting other parts of your business. This also means you’ll have more control over how much money you spend on upgrades each year because you can make those decisions based on actual usage patterns instead of guessing what might happen in a year.
The examples above show many ways to modernize mainframes without investing in new infrastructure and without significant disruption to your IT operations. The key is understanding what data is used in your mainframe applications and how other applications should access that data. If you do this right, you will create an environment where developers have access to distributed systems for free. You will also be able to focus your efforts on developing new apps instead of worrying about how they’ll be able to meet demand.