Use Case Deployment

In the Deployment phase, the solution is transitioned from staging environments to production. This phase ensures a secure, structured, and automated release across environments such as Dev, QA, UAT, and Production, using modern deployment modes and DevOps tooling integrated into Calibo Sandbox.

Deployment is fully automated using CI/CD pipelines orchestrated through Jenkins, with artifact management via JFrog, static code analysis through SonarQube, and vulnerability scanning using Qualys. Infrastructure provisioning is handled via Terraform, while the application components are containerized using Docker and deployed to scalable environments such as Kubernetes or OpenShift, across AWS or Azure.

Calibo Sandbox provides real-time visibility into pipeline status, logs, artifact URLs, and deployment outputs—empowering development and operations teams to troubleshoot quickly and deploy confidently.

By this stage, all code has been validated, tested, and approved. With infrastructure standardized and compliance guardrails enforced, deployment becomes a streamlined, low-risk process.

Goals	Outcome
Standardize deployments through predefined CI/CD pipelines and environment stages. Support flexible and agile, multi-stage deployment workflows (Dev, QA, UAT, Demo, Prod). Enable automated and efficient delivery through integrated CI/CD pipelines and provide real-time visibility and control over deployment activities. Support continuous delivery and rapid troubleshooting to reduce time-to-market.	Deployment pipeline established using Jenkins, JFrog, SonarQube, Qualys, and integrated secrets management. Secure and consistent deployment across Docker, Kubernetes, or OpenShift environments. Live application URLs generated post-deployment; accessible and verified. Deployment workflows enriched with rollback capabilities and version traceability. Use Case/MVP developed and deployed using platform-native CI/CD.

Goals

Outcome

Standardize deployments through predefined CI/CD pipelines and environment stages.
Support flexible and agile, multi-stage deployment workflows (Dev, QA, UAT, Demo, Prod).
Enable automated and efficient delivery through integrated CI/CD pipelines and provide real-time visibility and control over deployment activities.
Support continuous delivery and rapid troubleshooting to reduce time-to-market.

Deployment pipeline established using Jenkins, JFrog, SonarQube, Qualys, and integrated secrets management.
Secure and consistent deployment across Docker, Kubernetes, or OpenShift environments.
Live application URLs generated post-deployment; accessible and verified.
Deployment workflows enriched with rollback capabilities and version traceability.
Use Case/MVP developed and deployed using platform-native CI/CD.

Deployment Readiness Checklist

The Use Case Deployment Checklist ensures that the transition from development to live environments is secure, scalable, and production-ready. This checklist provides a structured deployment framework that validates automation workflows, infrastructure provisioning, and operational readiness while minimizing downtime and reducing risk.

Sl. No.	Item	Status (Started/Not Started/Completed)
1	CI/CD pipeline configuration validated (Jenkins, JFrog, SonarQube, Qualys)	Not Started
2	Infrastructure setup via Terraform competed (Dev, QA, UAT, Prod)	Not Started
3	Application deployed to Docker Container mode or Kubernetes, or OpenShift clusters from Sandbox	Not Started
4	Automated deployment pipeline executed (build, test, deploy)	Not Started
5	Real-time logs and error reports reviewed via Jenkins dashboard	Not Started
6	Post-deployment quality checks performed (SonarQube, Qualys)	Not Started
7	Live application URLs generated and validated	Not Started
8	Stakeholder approval obtained post UAT deployment	Not Started
9	Production readiness confirmed	Not Started

PRO TIP:

Always simulate a full deployment in UAT with live monitoring enabled. Catching issues here saves hours in production firefighting.
Maintain rollback scripts and tagged versions in Git for instant disaster recovery.

Advance Bank Deploying Sentiment Analysis Engine

After 9 weeks of focused development, collaboration, and sprint testing, the Advance Bank team had built a powerful sentiment analysis engine. It could analyze thousands of open-ended customer reviews and classify feedback in real-time. But delivering value meant more than just building it—it was time to deploy the solution to live environments.

Use Case: Sentiment Analysis of Customer Product Reviews

Goal: Deploy the validated, fully tested solution to production environments with speed, security, and reliability.

Step	Personas Involved	Description
Environment Setup	DevOps Engineer, Platform Engineer	Configured Dev, QA, UAT, and Prod environments in clicks
CI/CD Integration	DevOps Engineer QA Lead, Backend Developer	Jenkins pipelines orchestrated the CI/CD life cycle. Integrated tools included: JFrog for managing and storing build artifacts SonarQube for ensuring code quality gates Qualys for automated security scans
Secrets Management	DevOps Engineer Platform Security Lead	Validated secure secrets injection for all environments using integrated vaults (e.g., AWS Secrets Manager or Azure Key Vault). Ensured no credentials were hardcoded or exposed in logs.
Pipeline Execution	DevOps Engineer	Simply clicked Deploy. The backend and frontend components were neatly packaged into Docker containers. Jenkins pushed them straight to the configured Kubernetes cluster. If something failed, the system could bounce back within minutes. Each build passed through automated stages: Initialization, Build, Unit Tests, SonarQube Scan, Build Container Image, Publish Container Image, and Deploy.
Monitoring & Logs	QA Lead DevOps Engineer Product Owner	Real-time logs and error traces monitored via Jenkins dashboard and Calibo observability tools. Issues were resolved before promotion to the next environment.
Live Verification	QA Lead	Post-deployment smoke testing and UI validation were performed. API responses, visual layout, and data accuracy were verified. Live URLs were shared for stakeholder validation.
Release Confirmation	Product Release Manager Product Owner QA Lead	Final sign-off on the production deployment was obtained. Change log, build ID, and deployment summary were documented in Confluence. Stakeholders confirmed readiness for business usage.

What's Next:

With the sentiment analysis engine successfully deployed to UAT and Production, Advance Bank reached a key milestone. The live dashboard—fed by real-time feedback from e-commerce and app store channels—was now accessible to business leaders and customer-facing teams.

But deployment was just the beginning. The system began delivering live insights, highlighting sentiment trends and early signs of customer dissatisfaction. These real-world outputs now feed directly into the Business Validation & Refinement phase.

What's next? Business Validation and Refinement