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Home » Blogs » Six Mainframe DevOps Predictions for 2023

Six Mainframe DevOps Predictions for 2023

Avatar photoBy: John McKenny on February 24, 2023 Leave a Comment

DevOps, or the automation of application development and hand-off to operations, is more prevalent than ever on the mainframe. As we begin 2023, below are six predictions for what the coming year will bring. The composite themes will undoubtedly be speed, alignment and experience.

1. Enterprise DevOps Will Break Down Silos Across Core Systems

DevOps practices will continue to expand beyond cloud and distributed systems to include more core systems than ever before. Core platforms, including the mainframe, are the functional backbone of mission-critical applications that drive the world economy. In a recent IDC survey, 82% of organizations reported taking steps to prioritize using the same application development tools across distributed and mainframe teams. 2023 will continue to see a rise in enterprise DevOps that will bring down the silos between platforms. As core system workloads increase, organizations will see the benefit of aligning platforms such as mainframe and distributed to realize the full benefit of their DevOps initiatives.

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2. Growth in AI/ML as Key Components for Measuring DevOps Performance Metrics

The value of analytics will increase as organizations emphasize the use of key performance indicator (KPI) data to measure the performance and maturity of DevOps pipelines. In this way, organizations will not only gain more insights into their pipelines, but will also be able to proactively manage the data in real-time. They will be able to adjust loads as needed and create smart pipelines with advanced error handling. Organizations will leverage benchmarks within the analytics to see how they compare with their peers.

3. Cross-Functional Pipelines Will Create Hybrid Automated Everything

This will enable the cloud, core systems and data to deliver a more holistic view of “platforms, processes and people.” Silos will finally disappear. Automation will become a key priority in this effort as organizations seek to free up time for developers to innovate and achieve greater velocity, quality and efficiency. A single integrated and connected pipeline will emerge that automates the creation and testing of applications in a single flow. The rationale of prioritizing this alignment between distributed and core systems includes the reuse of best practices, workflow simplification, cost-effectiveness, application integration and streamlined training.

4. DevOps Will Embrace Platform Engineering and Site Reliability Engineering (SRE)

Platform engineering is the next evolution of DevOps, taking a DevOps platform and turning it into a self-service product to improve the developer experience. It treats DevOps as a product and allows organizations to create an end-to-end offering that serves developers, operations and platform teams. SRE focuses on the “Ops” side of DevOps, specifically how to automate operations capabilities. This is vital to the DevOps mission since it provides the data that allows organizations to manage what they can measure.

5. DevSecOps Will Play an Increasingly Central Role

Security must become a central part of the toolchain and a “first-class citizen” in the pipeline. Static application security testing (SAST), also known as white box testing, along with dynamic application security testing (DAST), also known as black box testing, will be further integrated into processes to enable security teams to scan large loads at rest or in real-time quickly and efficiently.

6. Git-Centric Enterprise Source Code Management (SCM) Strategy Will Grow

Git has become the de facto standard for SCM for distributed application workloads. In a recent IDC survey of industry professionals across 11 countries and multiple industries, 87% said they will migrate all or some of their mainframe source code to Git over the next three years. This means Git and Git-native development will become important capabilities for mainframe DevOps companies to support. Organizations will increasingly prefer implementation approaches and tools that offer flexibility in both scope and pace.

The techniques highlighted in these predictions are, naturally, related. They point to an ever-increasing capacity for efficiency and quality that will enable core and distributed technologies to work better together.

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Filed Under: Blogs, Business of DevOps, Continuous Delivery, DevOps in the Cloud, DevOps Practice, DevSecOps, Doin' DevOps Tagged With: ai, automation, devops, machine learning, mainframe

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