Making Mainframes DevOps-Friendly
Can you do DevOps on your mainframe? That might seem like a silly question. DevOps is among the newest trends in IT, and mainframes are an established, “legacy” technology. Pairing the two may appear to make little sense.
In fact, however, there are plenty of reasons to integrate mainframes into DevOps workflows. And thanks to technologies that make it easy to bridge the divide between mainframes and other infrastructure, doing DevOps on mainframes is not at all as difficult or strange as it might at first seem.
This article explains why DevOps on the mainframe is advantageous, and how organizations that have migrated to a DevOps-based workflow can make mainframes part of it.
First, though, let’s make clear what DevOps means.
Briefly, DevOps is what you get when you break down the barriers that have traditionally separated developers (the “Dev” people) from IT operations (the “Ops” folks). When these teams work in isolation from one another, lack of communication and coordination between them can lead to inefficiencies and delays, since the people who write code are out of sync with colleagues who deploy and manage it.
The goal behind the DevOps movement is to enable “continuous delivery.” This means that software is designed, written, tested and deployed to end users on a near-continuous basis, with no kinks or delays in the pipeline. Continuous delivery benefits everyone: it makes developers’ and admins’ jobs easier, it delivers application updates and enhancements to users faster, and it reduces time-to-value for organizations.
DevOps also emphasizes agility, meaning the ability to scale applications and switch between programming frameworks easily.
Why Do DevOps on the Mainframe?
So far, the DevOps conversation has focused mostly on newer programming languages and technologies. Infrastructure platforms, like Docker containers, and modern software development tools, such as continuous integration servers, are the technologies that first come to mind when most people hear “DevOps” today.
Yet this does not at all mean that mainframes should not also be a part of the DevOps conversation. As Compuware CEO Chris O’Malley notes, there are several reasons why bringing DevOps to the mainframe can benefit organizations. They include:
- Lots of business-critical applications depend on mainframes. If you want to deploy and maintain those applications with the efficiency that DevOps offers, you need to make mainframes part of your DevOps workflow.
- Although there are some significant differences between mainframes and other platforms – the programming languages are different, for example – mainframes are still just hardware and software platforms at the end of the day. There is no reason they can’t be integrated into a DevOps operation in the same way that any other platform could.
- Mainframes are an ideal location for processing information in a scalable and secure way. This flexibility makes them an excellent resource for organizations seeking to optimize their agility.
All of the above is to say that, although mainframes have not so far been a major part of the DevOps conversation, it’s time to change that. The organizations that do DevOps most effectively will realize that, while flashy technologies like Docker are one important component of DevOps, an optimal DevOps workflow involves all parts of an organization’s infrastructure.
How to Do DevOps on Mainframes
So, how do you actually integrate mainframes into your DevOps workflow? As noted above, making this connection can be a little difficult because mainframes environments are different in key respects from those of other systems.
Plus, making the move from a mainframe workflow that organizations have had in place for decades to a new, DevOps-centric one represents a big cultural jump. It’s much easier for a startup to do DevOps than it is for a company that already has a well-established workflow built around mainframe infrastructure.
Yet these challenges can be overcome using tools that connect mainframes seamlessly with the rest of an organization’s infrastructure. Consider, for example, Ironstream, Syncsort’s solution for moving machine data from mainframes into platforms hosted elsewhere. With Ironstream, you can easily perform tasks such as the following:
- Run analytics on mainframe data using Splunk, a user-friendly, comprehensive data analytics platform.
- Monitor and improve application performance using tools like Abend-AID, which is now integrated with Ironstream.
Similarly, solutions like DMX-h help to access and integrate mainframe data and diverse batch or streaming data sources with Hadoop and Spark for data analytics.
When it comes to DevOps, tools like these smooth over the barriers between mainframes and other infrastructure. They help to make workflows platform- and language-agnostic. That’s essential if all members of the software delivery team – developers, admins and everyone in between – is to collaborate seamlessly. It is also vital for achieving maximum agility.
As technology helps us close the gap between analytics and the mainframe, discover the six issues involved in Big Data deployments – and how to resolve them – by downloading Bloor Spotlight: Big Data and The Mainframe, Issues and Opportunities today!