Saturday, July 27, 2024

IBM Deployment Path to accelerates release lifecycle: Part 1

IBM Deployment lifecycle

Many companies reduce technical debt and satisfy CapEx-to-OpEx goals by moving to the cloud. This includes microservices rearchitecting, lift-and-shift, replatforming, refactoring, replacing, and more. As DevOps, cloud native, serverless, and SRE mature, the focus shifts to automation, speed, agility, and business alignment with IT, which helps enterprise IT become engineering organizations.

Many companies struggle to get value from their cloud investments and overspend. Multiple researchers claim that over 90% of firms overpay in cloud, frequently without significant returns.

When business and IT collaborate to quickly create new capabilities, developer productivity and speed to market increase, creating actual value. Some goals require a target operating model. Rapid cloud application IBM Deployment requires development acceleration with continuous integration, deployment, and testing (CI/CD/CT) and supply chain lifecycle acceleration, which involves governance risk and compliance (GRC), change management, operations, resiliency, and reliability. Enterprises seek solutions to help product teams deliver faster than ever.

Focus on automation and DevSecOps

Instead of adopting fast and scalable lifecycle and delivery models, enterprises retrofit cloud transformation into current application supply chain processes. Companies that automate the application lifecycle enable engineering-driven product lifecycle acceleration that realizes cloud transition. Some examples are:

  • Pattern-based architecture that standardizes architecture and design while letting teams choose patterns and technologies or co-create new patterns.
  • Patterns that ensure security and compliance traceability.
  • Codifying various cross-cutting concerns with patterns-as-code enhances pattern maturity and reusability.
  • Lifecycle-wide DevOps pipeline activities.
  • Automatic data production for security and compliance evaluations.
  • Reviewing operational preparedness without manual intervention.

The route from development to production is crucial to customer value as organizations adopt cloud native and everything as code. The “pathway to deploy,” or series of procedures and decisions, can greatly impact an organization’s capacity to deliver software rapidly, consistently, and at scale. The IBM Deployment process includes architecture, design, code IBM Deployment , testing, deployment, and monitoring. Each stage brings distinct difficulties and opportunities. IBM can help you find the methods and goal state mode for a seamless and effective deployment as you manage today’s complexity.

They will examine best practices, technologies, and processes that help organizations optimize software delivery pipelines, minimize time-to-market, improve software quality, and ensure production-ready operations.

Enterprise cloud-native software development is constantly changing, thus the second part in this series gives a maturity model and building blocks to speed the software supply chain lifecycle.

The IBM Deployment path: Current views and challenges

The SDLC diagram below shows common gates for enterprise software development. The flow is self-explanatory, but the essential is to grasp that the software supply chain process combines waterfall and intermittent agile approaches. The problem is that manual first- and last-mile actions affect the build-deploy timeline for an application or iteration.

IBM Deployment Path to accelerates release lifecycle: Part 1
Image Credit to IBM

The main issues with classical SDLC are:

  1. From architecture and design to development takes 4-8 weeks. The cause is:
    • Multiple first-mile reviews for privacy, data classification, business continuity, and regulatory compliance (most of which are manual).
    • Despite agile development concepts (e.g., environment provisioning only after full design approval), enterprise-wide SDLC processes are waterfall or semi-agile and require sequential execution.
    • Deep scrutiny and interventions with limited acceleration are applied to “unique” applications.
    • Institutionalizing patterns-based design and development is difficult due to lack of coherent effort and change agent driving, such as standardization.
    • Security controls and guidelines frequently require manual or semi-human operations, slowing development.
  2. Development environment provision and CI/CD/CT tooling integration wait time owing to:
    • Semi-automated or manual environment provisioning.
    • Paper patterns are just prescriptive.
    • Fragmented DevOps tools that require assembly.
  3. Post-development (last-mile) wait time before go-live is 6–8 weeks or more due to:
    • Manual evidence collection for security and compliance evaluations beyond SAST/SCA/DAST (security configuration, day 2 controls, tagging, etc.).
    • Manual evidence gathering for operation and resiliency reviews (cloud operations, business continuity).
    • Transition evaluations for IT service and issue management and resolution.

Launch path: Target state

A fast, efficient procedure that reduces bottlenecks and accelerates software supply chain transformation is needed to deliver goal state. The ideal IBM Deployment pathway integrates design (first mile), development, testing, platform engineering, and deployment (last mile) using agile and DevOps principles. With automated validations to production environments, code updates may be deployed quickly.

IBM’s target state integrates security checks and compliance validation into the CI/CD/CT pipeline to find and fix problems early. This concept promotes shared accountability across development, operations, reliability, and security teams. It also creates continual monitoring and feedback loops for improvement. The goal is to quickly deploy software upgrades and new features to end users with minimal manual intervention and high corporate stakeholder confidence.

The graphic below shows a target IBM Deployment pathway that supports cloud-native SDLC.

IBM Deployment Path to accelerates release lifecycle: Part 1
Image Credit to IBM

Features of the cloud-native SDLC model include:

  • Enterprise-wide pattern-driven architecture and design.
  • Coded patterns that meet security, compliance, resiliency, and other enterprise policies.
  • Pattern-accelerated security and compliance reviews that explain the solution.
  • Core development, encompassing platform engineering corporate catalog-driven environments, pipelines, and service configuration.
  • A CI/CD/CT pipeline that links all actions in the IBM Deployment lifecycle.
  • Platform engineering embeds enterprise policies like encryption in platform policies while building, configuring, and managing platforms and services.
  • Security and compliance tooling (vulnerability scans, policy checks) and pipeline-integrated or self-service automation.
  • High-volume log, tool, and code scan data generation for multiple evaluations without operator involvement.
  • Tracking backlog, IBM Deployment , release notes, and change impact.
  • Exceptional interventions.

Clear, accountable, and traceable IBM Deployment pathway accelerates

Organizations can standardize supply chain lifecycle processes and ensure traceability and auditability by adopting a standardized IBM Deployment strategy. This gives stakeholders real-time visibility into the program’s development from design to deployment. Assigning ownership at each point of the deployment route makes team members accountable for their deliverables, makes it easier to manage contributions and modifications, and speeds up issue resolution with the correct amount of intervention. Data-driven insights from traceability to deployment improve future program procedures and efficiency. Each step of the deployment process is documented and retrievable, making compliance with industry rules and reporting easier.

Drakshi
Drakshi
Since June 2023, Drakshi has been writing articles of Artificial Intelligence for govindhtech. She was a postgraduate in business administration. She was an enthusiast of Artificial Intelligence.
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