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Mainframe + Git + DevOps Pipelines = Agility Without Compromise

By Anshul Agrawal posted 4 days ago

  

For decades, mainframe development followed a traditional workflow — source stored in proprietary libraries, changes tracked manually, and deployments managed through rigid, batch-oriented processes. While this model has served business-critical systems well, it often lacks the agility, visibility, and automation that modern software delivery demands.

Today, with the rise of Git and DevOps pipelines, mainframe teams can modernize their workflows without compromising the stability and performance these platforms are known for.

Why Git for Mainframe?

Git brings version control capabilities that go beyond basic source storage. Developers can branch, merge, and collaborate in parallel — enabling multiple feature streams without stepping on each other’s work. Git’s distributed nature also allows developers to work offline and sync when ready, making it flexible for global teams.

On the mainframe, COBOL, PL/I, Assembler, JCL, and other artifacts can be managed just like Java or Python code. Using Git bridges the gap between mainframe and distributed developers, aligning everyone to a common toolset.

Integrating DevOps Pipelines

Once code is in Git, the next step is to integrate DevOps pipelines. A pipeline automates the steps from code commit to deployment, ensuring consistency and speed. For mainframe applications, this might include:

  • Automated build: Compiling COBOL or PL/I programs, assembling modules, and preparing JCL automatically upon commit.

  • Static analysis: Running quality and compliance checks early to catch issues before they hit production.

  • Automated testing: Executing unit, functional, and regression tests on test LPARs without manual intervention.

  • Packaging and deployment: Using tools like DBB (Dependency Based Build) or vendor solutions to deploy into integration or production environments.

Tooling and Integration Points

Git can be hosted on platforms like GitHub, GitLab, or Bitbucket, while pipelines can be orchestrated with Jenkins, GitHub Actions, or Azure DevOps. For mainframes, these pipelines often connect to tools such as:

  • IBM Dependency Based Build (DBB) for automated mainframe builds.

  • Zowe CLI for interacting with z/OS datasets, USS, and JES from the pipeline.

  • BMC AMI DevX or other vendor DevOps suites for deeper integration and automation.

Cultural Shift and Collaboration

Adopting Git and pipelines isn’t just a technical change — it’s a cultural shift. Developers need to embrace branching strategies, code reviews, and automated checks. Operations teams must collaborate more closely with development, as pipeline automation impacts how code moves through environments.

The payoff is substantial: faster delivery cycles, fewer manual errors, improved visibility, and the ability to integrate mainframe changes seamlessly into enterprise-wide DevOps processes.

Final Thoughts

Mainframe applications aren’t exempt from modern software practices. By adopting Git for source control and DevOps pipelines for automation, organizations can bring mainframe delivery in line with the rest of the enterprise — without sacrificing the reliability and performance that make the mainframe indispensable.

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