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Mastering Modern Mainframe DevOps; Strategies for Speed and Innovation

Mastering Modern Mainframe DevOps; Strategies for Speed and Innovation

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Mastering Modern Mainframe DevOps: Curriculum

Mastering Modern Mainframe DevOps: Strategies for Speed and Innovation

Unlock the power of modern DevOps practices on the mainframe. This comprehensive course provides you with the knowledge and skills to transform your mainframe environment, driving innovation, increasing velocity, and improving overall business outcomes. Through hands-on exercises, real-world case studies, and expert guidance, you'll learn to apply Agile principles, automation techniques, and collaboration strategies to the unique challenges of mainframe development and operations.

This course offers:

  • Interactive Learning: Engage with dynamic content, simulations, and collaborative exercises.
  • Engaging Content: Benefit from a curriculum designed to keep you motivated and focused.
  • Comprehensive Coverage: Master all aspects of mainframe DevOps, from foundational concepts to advanced techniques.
  • Personalized Experience: Tailor your learning path to your specific needs and goals.
  • Up-to-date Information: Stay current with the latest trends and best practices in mainframe DevOps.
  • Practical Skills: Develop hands-on expertise through real-world projects and simulations.
  • Real-World Applications: Apply your knowledge to solve actual business challenges.
  • High-Quality Content: Learn from expertly curated materials and resources.
  • Expert Instructors: Receive guidance from seasoned professionals with deep mainframe and DevOps experience.
  • Certification: Earn a valuable credential to demonstrate your expertise.
  • Flexible Learning: Study at your own pace, on your own schedule.
  • User-Friendly Platform: Enjoy a seamless and intuitive learning experience.
  • Mobile Accessibility: Access course materials anytime, anywhere.
  • Community-Driven Support: Connect with fellow learners and industry experts.
  • Actionable Insights: Gain practical knowledge you can apply immediately.
  • Hands-on Projects: Build a portfolio of real-world DevOps solutions.
  • Bite-Sized Lessons: Learn in manageable chunks for optimal retention.
  • Lifetime Access: Access course materials and updates indefinitely.
  • Gamification Elements: Enhance your learning experience with fun and engaging challenges.
  • Progress Tracking: Monitor your progress and identify areas for improvement.
Upon successful completion of this course, participants will receive a certificate issued by The Art of Service, validating their mastery of Modern Mainframe DevOps principles and practices.



Module 1: Introduction to Modern Mainframe DevOps

Overview:

This module introduces the core concepts of DevOps and its relevance to the mainframe environment. It establishes the foundation for understanding the benefits and challenges of implementing DevOps in a traditional mainframe setting.

  • Defining DevOps:
    • What is DevOps and why is it important?
    • The DevOps lifecycle: Plan, Code, Build, Test, Release, Deploy, Operate, Monitor
    • The Culture of DevOps: Collaboration, Communication, and Continuous Improvement
  • Mainframe Challenges and Opportunities:
    • Addressing common misconceptions about the mainframe.
    • Identifying the strengths and weaknesses of the mainframe in the context of DevOps.
    • Exploring opportunities to leverage mainframe capabilities for modern applications.
  • Why DevOps for Mainframe?:
    • Faster time to market for mainframe applications.
    • Improved application quality and reliability.
    • Increased collaboration between development and operations teams.
    • Enhanced responsiveness to business needs.
  • Introduction to Agile Methodologies on Mainframe:
    • Understanding Agile principles and values
    • Scrum, Kanban and other Agile frameworks on the mainframe.
    • Iterative development and incremental delivery on z/OS.
  • DevOps Toolchain Overview for Mainframe:
    • Introduction to various tools used in the Mainframe DevOps lifecycle.
    • Categorization of tools: Source code management, build automation, testing, deployment, monitoring.
    • Understanding the integration between different tools.


Module 2: Source Code Management and Version Control

Overview:

This module focuses on implementing effective source code management practices using modern tools and techniques. It covers Git and other version control systems and their integration with the mainframe.

  • Introduction to Git for Mainframe:
    • Understanding Git concepts: repositories, branches, commits, merges.
    • Setting up a Git environment for mainframe development.
    • Using Git commands for basic source code management tasks.
  • Integrating Git with z/OS:
    • Using Git clients on z/OS.
    • Integrating Git with mainframe IDEs like IBM Developer for z/OS (IDz).
    • Managing mainframe artifacts (e.g., COBOL, JCL) in Git repositories.
  • Branching Strategies for Mainframe Development:
    • Understanding different branching models (e.g., Gitflow, GitHub Flow).
    • Implementing branching strategies for feature development, bug fixes, and releases.
    • Best practices for managing branches in a mainframe environment.
  • Code Review and Collaboration:
    • Using Git pull requests for code review.
    • Implementing code review workflows for mainframe projects.
    • Leveraging collaboration tools for effective communication and feedback.
  • Security Considerations for Source Code Management:
    • Controlling access to Git repositories.
    • Securing sensitive data in source code.
    • Auditing Git activity for security compliance.
  • Migrating from Traditional SCMs to Git:
    • Challenges of migrating from Endevor, ChangeMan ZMF, or CA Panvalet to Git.
    • Strategies for a phased migration approach.
    • Tools and techniques to automate the migration process.


Module 3: Build Automation and Continuous Integration

Overview:

This module explores the principles of build automation and continuous integration (CI) in the mainframe context. It covers tools like Jenkins, Bamboo, and GitLab CI and their integration with mainframe build systems.

  • Introduction to Build Automation:
    • Understanding the benefits of build automation.
    • Identifying the key components of a build automation system.
    • Best practices for creating reliable and repeatable builds.
  • Implementing Continuous Integration:
    • Setting up a CI server (e.g., Jenkins, Bamboo, GitLab CI).
    • Configuring CI pipelines for mainframe projects.
    • Automating build, test, and deployment processes.
  • Mainframe Build Systems:
    • Understanding traditional mainframe build systems (e.g., ISPF, TSO CLISTs).
    • Integrating modern build tools (e.g., Ant, Maven, Gradle) with mainframe build processes.
    • Using build automation tools to streamline mainframe builds.
  • Dependency Management:
    • Managing dependencies between mainframe components.
    • Using dependency management tools to ensure build consistency.
    • Best practices for handling mainframe library dependencies.
  • Automated Code Quality Checks:
    • Integrating static code analysis tools (e.g., SonarQube) into the CI pipeline.
    • Enforcing coding standards and best practices.
    • Identifying and addressing code quality issues early in the development cycle.
  • Security Integration in the Build Process:
    • Integrating security scanning tools (e.g., SAST, DAST) into the CI pipeline.
    • Identifying and mitigating security vulnerabilities during the build process.
    • Implementing security gates to prevent vulnerable code from being deployed.


Module 4: Automated Testing and Quality Assurance

Overview:

This module focuses on automating testing processes to ensure the quality and reliability of mainframe applications. It covers unit testing, integration testing, system testing, and performance testing, along with tools and techniques for each.

  • Test Automation Fundamentals:
    • Understanding the benefits of test automation.
    • Identifying different types of tests (e.g., unit, integration, system, performance).
    • Developing a test automation strategy for mainframe applications.
  • Unit Testing on the Mainframe:
    • Using unit testing frameworks for COBOL and other mainframe languages.
    • Writing effective unit tests to verify the functionality of individual components.
    • Integrating unit tests into the CI pipeline.
  • Integration and System Testing:
    • Automating integration tests to verify the interactions between different components.
    • Performing system testing to ensure the application meets overall requirements.
    • Using test automation tools to streamline integration and system testing.
  • Performance Testing:
    • Conducting performance tests to identify bottlenecks and performance issues.
    • Using performance testing tools (e.g., JMeter, LoadRunner) to simulate user load.
    • Optimizing mainframe applications for performance.
  • Test Data Management:
    • Creating and managing test data for automated tests.
    • Using test data management tools to generate and mask sensitive data.
    • Ensuring test data accuracy and consistency.
  • Service Virtualization for Mainframe:
    • Understanding the need for service virtualization in mainframe testing.
    • Using service virtualization tools (e.g., Parasoft Virtualize, CA Service Virtualization) to simulate dependencies.
    • Reducing test environment dependencies and improving test coverage.


Module 5: Configuration Management and Infrastructure as Code

Overview:

This module covers configuration management and infrastructure as code (IaC) principles and practices in the mainframe environment. It explores tools like Ansible, Chef, and Puppet for automating infrastructure provisioning and configuration.

  • Introduction to Configuration Management:
    • Understanding the benefits of configuration management.
    • Identifying the key components of a configuration management system.
    • Best practices for managing mainframe configurations.
  • Infrastructure as Code (IaC) on Mainframe:
    • Defining mainframe infrastructure as code using tools like Ansible.
    • Automating the provisioning and configuration of mainframe resources.
    • Managing infrastructure changes in a controlled and repeatable manner.
  • Configuration Management Tools:
    • Using Ansible, Chef, and Puppet to manage mainframe configurations.
    • Configuring mainframe middleware (e.g., CICS, IMS, DB2) using configuration management tools.
    • Automating the deployment of mainframe applications.
  • Version Control for Infrastructure:
    • Storing infrastructure configuration files in Git repositories.
    • Tracking changes to infrastructure configurations over time.
    • Using version control to manage different environments (e.g., development, testing, production).
  • Security Considerations for Configuration Management:
    • Securing access to configuration management systems.
    • Protecting sensitive data in configuration files.
    • Auditing configuration changes for security compliance.
  • Containerization on Mainframe with z/OS Container Extensions (zCX):
    • Introduction to zCX and its role in modernizing mainframe applications.
    • Deploying containerized applications on z/OS using zCX.
    • Managing and scaling containerized workloads on the mainframe.


Module 6: Deployment Automation and Continuous Delivery

Overview:

This module focuses on automating the deployment process and implementing continuous delivery (CD) pipelines for mainframe applications. It covers tools like IBM UrbanCode Deploy, CA Release Automation, and XL Deploy.

  • Deployment Automation Fundamentals:
    • Understanding the benefits of deployment automation.
    • Identifying the key components of a deployment automation system.
    • Best practices for automating mainframe deployments.
  • Implementing Continuous Delivery:
    • Setting up a CD pipeline for mainframe applications.
    • Automating the deployment of applications to different environments.
    • Integrating deployment automation with the CI pipeline.
  • Deployment Automation Tools:
    • Using IBM UrbanCode Deploy, CA Release Automation, and XL Deploy to automate mainframe deployments.
    • Configuring deployment workflows for different types of applications.
    • Managing deployment artifacts and dependencies.
  • Rollback Strategies:
    • Implementing automated rollback procedures to handle deployment failures.
    • Testing rollback procedures to ensure they work as expected.
    • Minimizing the impact of deployment failures on users.
  • Monitoring and Logging:
    • Monitoring deployment processes to identify potential issues.
    • Logging deployment activity for auditing and troubleshooting.
    • Integrating deployment monitoring with overall system monitoring.
  • Blue/Green Deployments on Mainframe:
    • Understanding the benefits of blue/green deployments.
    • Implementing blue/green deployment strategies for mainframe applications.
    • Minimizing downtime and risk during deployments.


Module 7: Monitoring and Observability

Overview:

This module explores the importance of monitoring and observability in a mainframe DevOps environment. It covers tools like Splunk, ELK Stack, and Dynatrace for monitoring mainframe applications and infrastructure.

  • Introduction to Monitoring and Observability:
    • Understanding the difference between monitoring and observability.
    • Identifying the key metrics to monitor in a mainframe environment.
    • Best practices for setting up effective monitoring systems.
  • Monitoring Tools for Mainframe:
    • Using Splunk, ELK Stack (Elasticsearch, Logstash, Kibana), and Dynatrace to monitor mainframe applications and infrastructure.
    • Collecting and analyzing log data from mainframe systems.
    • Creating dashboards and alerts to identify potential issues.
  • Application Performance Monitoring (APM):
    • Using APM tools to monitor the performance of mainframe applications.
    • Identifying performance bottlenecks and areas for optimization.
    • Tracking application performance over time.
  • Infrastructure Monitoring:
    • Monitoring the health and performance of mainframe hardware and software components.
    • Tracking resource utilization (e.g., CPU, memory, disk) on mainframe systems.
    • Identifying potential infrastructure issues before they impact applications.
  • Alerting and Incident Management:
    • Setting up alerts to notify teams of potential issues.
    • Integrating monitoring systems with incident management tools.
    • Automating incident response procedures.
  • AI-Powered Monitoring for Mainframe:
    • Using AI and machine learning to improve mainframe monitoring.
    • Detecting anomalies and predicting potential issues.
    • Automating root cause analysis and incident remediation.


Module 8: Security and Compliance in Mainframe DevOps

Overview:

This module focuses on integrating security and compliance considerations into the mainframe DevOps lifecycle. It covers topics like security scanning, access control, and auditing.

  • Security as Code:
    • Integrating security practices into the DevOps pipeline.
    • Automating security scanning and vulnerability assessments.
    • Enforcing security policies through code.
  • Access Control and Authentication:
    • Implementing strong access control policies for mainframe resources.
    • Using multi-factor authentication to protect against unauthorized access.
    • Managing user identities and permissions.
  • Data Security:
    • Protecting sensitive data at rest and in transit.
    • Using encryption and data masking techniques to secure data.
    • Complying with data privacy regulations (e.g., GDPR, CCPA).
  • Auditing and Compliance:
    • Logging and auditing security events.
    • Generating compliance reports for regulatory agencies.
    • Automating compliance checks to ensure adherence to policies.
  • Vulnerability Management:
    • Identifying and prioritizing vulnerabilities in mainframe systems.
    • Patching and remediating vulnerabilities in a timely manner.
    • Using vulnerability scanning tools to detect and prevent vulnerabilities.
  • Integrating Security into Mainframe Development:
    • Implementing secure coding practices.
    • Training developers on security best practices.
    • Building security into the design and architecture of mainframe applications.


Module 9: Mainframe DevOps Toolchain Deep Dive

Overview:

This module provides an in-depth look at specific tools commonly used in a modern Mainframe DevOps environment. This module includes tools like Zowe, IBM Developer for z/OS (IDz), and Endevor.

  • Zowe: An Open Mainframe Framework:
    • Understanding Zowe architecture and components.
    • Using Zowe CLI to interact with the mainframe.
    • Exploring Zowe APIs for automation and integration.
    • Customizing Zowe for your specific environment.
  • IBM Developer for z/OS (IDz):
    • Using IDz for modern mainframe development.
    • Integrating IDz with Git and other DevOps tools.
    • Leveraging IDz features for code quality and testing.
    • Customizing IDz to improve developer productivity.
  • Endevor: Integrating Legacy SCM with DevOps:
    • Understanding Endevor's role in mainframe development.
    • Integrating Endevor with CI/CD pipelines.
    • Using Endevor APIs for automation.
    • Best practices for managing mainframe code with Endevor in a DevOps environment.
  • Exploring Other Key Tools:
    • Understanding other key Mainframe DevOps tools.
    • Tools like: Compuware Topaz, CA Brightside, Rocket DevOps, Micro Focus Enterprise Suite.
    • Understanding various roles and their integrations.
  • Choosing the Right Tools for Your Needs:
    • Understanding various needs.
    • Understanding your current technology stack.
    • Determining your budget and future road map.


Module 10: Mainframe DevOps Transformation Strategies

Overview:

This module provides guidance on planning and executing a successful DevOps transformation in a mainframe environment. It covers organizational changes, cultural shifts, and technology implementations.

  • Assessing Your Current State:
    • Evaluating your current mainframe environment.
    • Identifying areas for improvement.
    • Setting realistic goals for your DevOps transformation.
  • Building a DevOps Roadmap:
    • Creating a phased approach to DevOps implementation.
    • Prioritizing initiatives based on business value and feasibility.
    • Defining key milestones and metrics.
  • Organizational and Cultural Changes:
    • Breaking down silos between development and operations teams.
    • Promoting collaboration and communication.
    • Empowering teams to make decisions and take ownership.
  • Technology Implementation:
    • Selecting the right tools and technologies for your mainframe environment.
    • Integrating DevOps tools with existing systems.
    • Automating key processes to improve efficiency.
  • Measuring Success:
    • Tracking key metrics to measure the impact of your DevOps transformation.
    • Regularly reviewing and adjusting your roadmap based on results.
    • Celebrating successes and learning from failures.
  • Change Management Best Practices:
    • Communication strategies for DevOps adoption.
    • Training and skill development for mainframe teams.
    • Addressing resistance to change and fostering a DevOps mindset.


Module 11: Integrating Mainframe with Cloud and Distributed Systems

Overview:

This module focuses on strategies for integrating mainframe applications with cloud and distributed systems, enabling hybrid cloud solutions and modernizing legacy applications.

  • Hybrid Cloud Architectures for Mainframe:
    • Understanding different hybrid cloud models (e.g., public, private, hybrid).
    • Designing hybrid cloud architectures that leverage mainframe strengths.
    • Choosing the right cloud platform for your mainframe integration needs.
  • APIs and Microservices on Mainframe:
    • Exposing mainframe functionality through APIs.
    • Building microservices on mainframe using modern technologies.
    • Integrating mainframe microservices with cloud-based applications.
  • Data Integration Strategies:
    • Moving data between mainframe and cloud systems.
    • Using data integration tools to synchronize data across platforms.
    • Addressing data security and compliance concerns in hybrid environments.
  • Cloud-Native Development on Mainframe:
    • Adopting cloud-native development practices on the mainframe.
    • Using containers and orchestration platforms on z/OS.
    • Building scalable and resilient applications that run across mainframe and cloud.
  • Security Considerations in Hybrid Environments:
    • Securing data and applications across mainframe and cloud systems.
    • Implementing consistent security policies across platforms.
    • Managing identities and access control in hybrid environments.
  • Case Studies: Successful Mainframe-Cloud Integration:
    • Real-world examples of organizations integrating mainframe with cloud platforms.
    • Lessons learned and best practices for hybrid cloud implementations.


Module 12: Mainframe DevOps Automation Best Practices

Overview:

This module explores advanced automation techniques and best practices for streamlining Mainframe DevOps processes and maximizing efficiency.

  • Advanced Scripting and Automation Techniques:
    • Using scripting languages (e.g., REXX, Python) to automate mainframe tasks.
    • Creating custom automation scripts for specific use cases.
    • Integrating scripting with DevOps tools and platforms.
  • Workflow Orchestration:
    • Designing and implementing automated workflows for complex processes.
    • Using workflow orchestration tools to manage and coordinate tasks.
    • Optimizing workflows for speed and efficiency.
  • Event-Driven Automation:
    • Triggering automation tasks based on events and alerts.
    • Using event-driven architectures to automate responses to critical events.
    • Integrating event-driven automation with monitoring and alerting systems.
  • Self-Service Automation:
    • Empowering users to automate tasks and provision resources on demand.
    • Building self-service portals for common mainframe operations.
    • Improving user satisfaction and reducing manual effort.
  • Leveraging AI for Automation:
    • Using AI and machine learning to automate complex tasks.
    • Optimizing automation processes based on data analysis.
    • Predicting and preventing potential issues with AI-powered automation.
  • Best Practices for Maintaining Automation Scripts and Workflows:
    • Version controlling automation scripts and workflows.
    • Testing and documenting automation procedures.
    • Implementing change management processes for automation scripts.


Module 13: Mainframe DevOps and the Software Development Lifecycle

Overview:

This module examines how DevOps practices can be integrated throughout the entire Software Development Lifecycle (SDLC) to improve agility and quality.

  • DevOps in Requirements Gathering and Analysis:
    • Using agile methodologies to gather requirements iteratively.
    • Collaborating with stakeholders to define clear and measurable requirements.
    • Automating requirements traceability and validation.
  • DevOps in Design and Architecture:
    • Designing applications for continuous integration and delivery.
    • Using microservices architecture to improve modularity and scalability.
    • Adopting secure design principles to protect against vulnerabilities.
  • DevOps in Development and Coding:
    • Using version control and code review practices to improve code quality.
    • Automating code analysis and static testing.
    • Implementing test-driven development (TDD) to ensure code correctness.
  • DevOps in Testing and Quality Assurance:
    • Automating unit, integration, and system testing.
    • Using test data management techniques to create realistic test environments.
    • Implementing continuous testing to identify and resolve issues early.
  • DevOps in Deployment and Release:
    • Automating the deployment process to reduce errors and downtime.
    • Using continuous delivery pipelines to release software frequently.
    • Implementing rollback strategies to handle deployment failures.
  • DevOps in Operations and Maintenance:
    • Monitoring application performance and infrastructure health.
    • Automating incident response and remediation.
    • Continuously improving application performance and stability.


Module 14: Implementing a DevOps Mindset on the Mainframe

Overview:

This module focuses on cultivating a DevOps mindset within mainframe teams, emphasizing collaboration, communication, and continuous improvement.

  • Understanding the DevOps Culture:
    • Defining the core principles of DevOps culture: collaboration, communication, automation, measurement, and sharing.
    • Identifying the key behaviors that support a DevOps mindset.
    • Overcoming common cultural barriers to DevOps adoption.
  • Building Cross-Functional Teams:
    • Creating teams that include members from development, operations, security, and other relevant areas.
    • Empowering teams to make decisions and take ownership of their work.
    • Providing teams with the resources and support they need to succeed.
  • Improving Communication and Collaboration:
    • Using communication tools and techniques to facilitate collaboration.
    • Creating a culture of open communication and feedback.
    • Holding regular team meetings and retrospectives.
  • Embracing Continuous Improvement:
    • Using data to identify areas for improvement.
    • Experimenting with new tools and techniques.
    • Sharing lessons learned and best practices with others.
  • Promoting a Learning Culture:
    • Encouraging team members to learn new skills and technologies.
    • Providing access to training and development opportunities.
    • Creating a culture of experimentation and innovation.
  • Leading by Example:
    • Demonstrating a commitment to DevOps principles and practices.
    • Encouraging others to adopt a DevOps mindset.
    • Celebrating successes and recognizing accomplishments.


Module 15: Mainframe DevOps Metrics and Measurement

Overview:

This module explores key metrics for measuring the success of a Mainframe DevOps implementation and provides guidance on establishing a data-driven approach to continuous improvement.

  • Identifying Key DevOps Metrics:
    • Defining metrics that align with business goals and DevOps objectives.
    • Understanding the difference between leading and lagging indicators.
    • Selecting metrics that are easy to measure and track.
  • Common Mainframe DevOps Metrics:
    • Lead Time: Time to deliver a new feature from concept to production.
    • Deployment Frequency: How often code is deployed to production.
    • Change Failure Rate: Percentage of deployments that cause incidents.
    • Mean Time To Recovery (MTTR): Average time to restore service after an incident.
    • Throughput: Volume of work completed per unit of time.
    • Customer Satisfaction: Measure of customer happiness with software.
  • Tools for Tracking and Visualizing Metrics:
    • Using monitoring tools to collect data on key metrics.
    • Creating dashboards to visualize metrics and track progress.
    • Integrating metrics with CI/CD pipelines and other DevOps tools.
  • Establishing Baselines and Targets:
    • Defining a baseline for each metric to measure improvement over time.
    • Setting realistic targets for each metric based on business goals.
    • Tracking progress towards targets and adjusting strategies as needed.
  • Using Metrics to Drive Continuous Improvement:
    • Analyzing metrics to identify areas for improvement.
    • Experimenting with new tools and techniques to improve performance.
    • Sharing metrics with teams to promote transparency and accountability.
  • Avoiding Common Pitfalls in Metrics and Measurement:
    • Focusing on vanity metrics that don't drive business value.
    • Using metrics to punish or blame individuals.
    • Ignoring the context of metrics and making decisions based solely on data.


Module 16: Mainframe DevOps for Regulated Industries

Overview:

This module examines the specific challenges and considerations for implementing DevOps in regulated industries such as finance, healthcare, and government, ensuring compliance and security.

  • Understanding Regulatory Requirements:
    • Identifying the specific regulatory requirements that apply to your industry.
    • Interpreting regulations and translating them into actionable DevOps practices.
    • Staying up-to-date on changes to regulations and adapting your DevOps processes accordingly.
  • Compliance as Code:
    • Automating compliance checks and audits.
    • Embedding security and compliance requirements into the CI/CD pipeline.
    • Using policy-as-code to enforce regulatory requirements.
  • Security and Access Control:
    • Implementing strong access control policies to protect sensitive data.
    • Using multi-factor authentication to prevent unauthorized access.
    • Auditing access to systems and data to ensure compliance.
  • Data Privacy and Protection:
    • Complying with data privacy regulations such as GDPR and CCPA.
    • Implementing data masking and encryption techniques to protect sensitive data.
    • Managing data retention policies and ensuring data is disposed of properly.
  • Change Management and Audit Trails:
    • Implementing robust change management processes to track changes to systems and data.
    • Maintaining detailed audit trails to demonstrate compliance with regulations.
    • Using audit trails to investigate incidents and identify potential security breaches.
  • Working with Auditors:
    • Preparing for audits by gathering documentation and evidence of compliance.
    • Working with auditors to address their concerns and answer their questions.
    • Using audit findings to improve DevOps processes and enhance compliance.


Module 17: Mainframe DevOps and Modernization Strategies

Overview:

This module explores how DevOps practices can facilitate the modernization of mainframe applications and infrastructure, enabling greater agility and business value.

  • Defining Mainframe Modernization:
    • Understanding the different approaches to mainframe modernization (e.g., rehosting, replatforming, refactoring, rearchitecting).
    • Identifying the goals and objectives of your modernization efforts.
    • Selecting the right modernization strategy for your specific needs.
  • DevOps for Rehosting and Replatforming:
    • Using DevOps principles and practices to streamline rehosting and replatforming projects.
    • Automating the migration of applications and data to new platforms.
    • Ensuring that applications are properly tested and validated after migration.
  • DevOps for Refactoring and Rearchitecting:
    • Using DevOps to facilitate the refactoring and rearchitecting of mainframe applications.
    • Breaking down monolithic applications into smaller, more manageable components.
    • Adopting microservices architecture to improve scalability and resilience.
  • Integrating Modern Technologies with the Mainframe:
    • Using APIs and microservices to integrate the mainframe with modern technologies.
    • Exposing mainframe functionality to cloud-based applications.
    • Leveraging modern development tools and frameworks to build new applications on the mainframe.
  • DevOps and Continuous Delivery of Modernized Applications:
    • Using DevOps to enable continuous delivery of modernized applications.
    • Automating the deployment of applications to different environments.
    • Implementing rollback strategies to handle deployment failures.
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