Mohamed Amine Ben Fatma

Project: The Capture & Restore (C/R) process for Amadeus applications is designed to capture production databases and restore them step by step, incorporating data customization for both commercial and fictional airlines. To ensure the robustness and reliability of the cloud-based C/R solution, a comprehensive test suite has been developed. This suite rigorously validates the automated orchestration of the process and scripts, ensuring smooth execution and resilience across various scenarios. Activities performed: Execution of Ansible Playbooks via AWX Retrieve, analyze, and execute existing playbooks. Validate outputs (return codes, logs, database states). Debug failures and modify variables to test different scenarios. Simulated API Tests Identify and use simulated APIs to validate requests/responses. Test success, failure, and timeout scenarios. Log interactions for debugging purposes. End-to-End (E2E) Tests Execute key test cases (success, partial/total failure). Analyze logs and compare expected vs. actual results. Refine test coverage in collaboration with other teams. Jenkins Pipeline Automation Trigger and monitor playbook execution via Jenkins. Debug pipeline logs and adjust configurations as needed. Ensure automated generation of test reports. Test Deployment on Kubernetes Validate Jenkins execution and test scripts within Kubernetes pods. Monitor deployments, restart failed jobs, and verify connectivity. Final Deliverables: Successfully executed playbooks Validated API responses Automated E2E test reports Fully integrated Jenkins pipeline Stable Kubernetes deployment

Project: Amadeus emphasizes disruption testing, particularly Chaos testing. This initiative aims to assess system resilience and stability in the face of unexpected failures, network issues, and other chaotic conditions. The goal is to simulate real-world disruptions to ensure Amadeus' applications and services can withstand failures and recover effectively, ensuring high availability, strong performance, and optimal user satisfaction. By investing in Chaos testing, Amadeus aims to identify vulnerabilities, strengthen system robustness, and optimize disaster recovery processes. Activities performed: Improvement of the Chaos Testing Framework Integrate Chaos Toolkit, Litmus Operator, and OpenShift for automated chaos experiments. Develop failure scenarios (e.g., crashes, network issues, resource exhaustion). Automate test execution within Kubernetes. Adoption and Client Support Provide training, documentation, and integration support. Assist in framework integration and customization. Collect feedback for continuous improvement. Integration with Azure Leverage Azure Chaos Studio to test cloud-specific failures. Adapt the framework for AKS (pod crashes, resource limits, network issues). Optimize the ecosystem for cloud-native resilience. Ongoing Azure Support Track Azure updates and integrate new features. Expand cloud-specific failure scenarios. Optimize framework scalability and performance. Final Deliverables: Integrated chaos tests for on-premise and Azure environments Tailored chaos experiments for Azure Training and support for smooth adoption Scalable and optimized cloud resilience testing

Project: The objective of this mission is to automate data collection and develop an interactive graphical dashboard to efficiently visualize key indicators, ensuring clear analysis and optimized tracking. Activities performed: Identification of Metric Sources Define key KPIs from Jenkins, Terraform, Jira, Kubernetes, etc. Document data sources to ensure alignment with stakeholders. Metric Collection System Retrieve data via APIs, logs, or streaming. Normalize and store metrics in PostgreSQL, InfluxDB, or Prometheus. Schedule automated data collection. Metric Visualization Build interactive dashboards with Kibana and Power BI. Implement charts, filters, and alerts for real-time analysis. CI/CD Automation Automate data extraction and dashboard updates via Jenkins. Ensure self-healing mechanisms for failed tasks. Optimize performance for processing large data volumes. Final Deliverables: Automated data collection pipeline Real-time interactive dashboards Continuous updates via CI/CD Proactive alert system