CI/CD Automation

Designed and implemented a robust CI/CD pipeline to automate the software development lifecycle, ensuring faster and more reliable delivery of applications. The pipeline is integrated with the tools like Jenkins, Git, TFS, Dotnet Build, Docker, Kubernetes to streamline code building, testing, and deployment. It aimed to enhance code quality through automated testing with JMeter and SonarQube while ensuring secure and efficient artifact management using Jfrog. Kong and Kong mesh are used for managing API traffic and service communication, with Jaeger providing endto-end tracing and monitoring. The goal was to improve deployment frequency, reduce lead time, andenhance overall system stability.

• Jenkins – CI/CD Automation Server
• Git – Source Code Management
• TFS – Version Control and Work Item Tracking
• Docker – Containerization Platform
• Docker Registry – Storage for Container Images
• Kubernetes – Container Orchestration
• JMeter – Performance Testing
• Kong – API Gateway
• Kongmesh – Service Mesh for Microservices
• Jaeger – Distributed Tracing
• JFrog – Artifact Management
• SonarQube – Code Quality and Security Analysis

• Source Code Management (Git/TFS): Git and TFS were configured for version control with
  defined branch strategies (e.g., feature, develop, release).
• Jenkins Setup and Pipeline Configuration: Jenkins was installed and configured with a multibranch pipeline using a Jenkinsfile to pull code from Git/TFS.
• Build Automation with Docker: A Dockerfile was created to containerize the application, and Docker images were built and tagged with version and commit ID in Jenkins.
• Static Code Analysis with SonarQube: SonarQube was integrated with Jenkins to perform static code analysis, and builds were configured to fail if code quality thresholds were not met.
• Artifact Management with JFrog: JFrog was configured as the artifact repository, and Docker images and binaries were pushed to it.
• Deployment to Kubernetes: Kubernetes manifests and Helm charts were created, and Docker containers were deployed using Helm with rolling updates for zero downtime.
• API Management with Kong and Kongmesh: Kong was configured for API routing and authentication, and Kongmesh was set up for secure service-to-service communication using mTLS.
• Performance Testing with JMeter: JMeter test scripts were created and executed as part of the Jenkins pipeline, with test results analyzed and reported.
• Distributed Tracing with Jaeger: Jaeger was installed and configured to collect trace data from microservices, providing performance insights and debugging support.
• Monitoring and Alerting: Prometheus & Grafana was set up for dashboards, alerts were configured based on health and performance metrics, and notifications were automated.

Faster Release Cycles: Automated builds, tests, and deployments reduced release time by 40% and increased deployment frequency by 30%, enabling quicker delivery of new features and fixes.

Improved Code Quality: SonarQube integration ensured consistent code quality and reduced code issues by identifying bugs and vulnerabilities early in the pipeline.

Cost Savings: Automation of build, test, and deployment processes reduced manual effort by 60% and deployment errors by 45%, leading to lower operational costs and improved efficiency.

Higher Reliability and Availability: Rolling updates and automated rollbacks ensured zero downtime and increased deployment success rate by 35%, enhancing system stability.

Better Monitoring and Debugging: Jaeger and Grafana provided real-time insights into system health and performance, reducing debugging time by 50% and improving incident response.

Enhanced Security and API Management: Kong and Kongmesh ensured secure API routing, authentication, and service-to-service communication using mTLS, improving overall security and reducing latency.

Scalability and Resource Optimization: Kubernetes orchestration enabled rapid scaling and improved resource utilization by 30%, ensuring efficient handling of high traffic.