{"id":48088121,"url":"https://github.com/dbsectrainer/ai_cloud_dashboard","last_synced_at":"2026-04-04T15:25:50.189Z","repository":{"id":279146020,"uuid":"924416382","full_name":"dbsectrainer/ai_cloud_dashboard","owner":"dbsectrainer","description":null,"archived":false,"fork":false,"pushed_at":"2025-11-18T00:48:38.000Z","size":996,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-18T02:24:35.605Z","etag":null,"topics":["domain-cloud","lifecycle-maintenance","tech-python","type-edu"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dbsectrainer.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"docs/security.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-01-30T00:37:40.000Z","updated_at":"2025-10-16T02:47:34.000Z","dependencies_parsed_at":"2025-02-24T03:28:49.376Z","dependency_job_id":"9dadb97c-38b0-477a-8ebb-e833829a4a0c","html_url":"https://github.com/dbsectrainer/ai_cloud_dashboard","commit_stats":null,"previous_names":["dbsectrainer/ai_cloud_dashboard"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dbsectrainer/ai_cloud_dashboard","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbsectrainer%2Fai_cloud_dashboard","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbsectrainer%2Fai_cloud_dashboard/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbsectrainer%2Fai_cloud_dashboard/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbsectrainer%2Fai_cloud_dashboard/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dbsectrainer","download_url":"https://codeload.github.com/dbsectrainer/ai_cloud_dashboard/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbsectrainer%2Fai_cloud_dashboard/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31403958,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-04T10:20:44.708Z","status":"ssl_error","status_checked_at":"2026-04-04T10:20:06.846Z","response_time":60,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["domain-cloud","lifecycle-maintenance","tech-python","type-edu"],"created_at":"2026-04-04T15:25:49.210Z","updated_at":"2026-04-04T15:25:50.150Z","avatar_url":"https://github.com/dbsectrainer.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Global AI \u0026 Cloud Intelligence Dashboard 🌐\n\nA comprehensive real-time analytics platform for monitoring and analyzing the global AI and cloud computing landscape. This enterprise-grade dashboard provides strategic intelligence for decision-makers, offering deep insights into market trends, performance metrics, and competitive analysis.\n\n![Dashboard Preview](docs/diagrams/images/ai_cloud_dashboard.png)\n\n## 🚀 Key Features\n\n- **Market Intelligence**\n  - Real-time market share analysis\n  - Growth trend visualization\n  - Regional market dynamics\n  - Competitive landscape analysis\n\n- **Security \u0026 Compliance**\n  - Compliance requirement tracking\n  - Security score monitoring\n  - Certification timeline management\n  - Data residency visualization\n\n- **Cost Analysis**\n  - Total Cost of Ownership (TCO) calculator\n  - Provider cost comparisons\n  - Budget optimization tools\n  - Resource utilization tracking\n\n- **Performance Metrics**\n  - Real-time performance monitoring\n  - Global latency analysis\n  - SLA compliance tracking\n  - Resource efficiency metrics\n\n- **Strategic Tools**\n  - AI-powered decision support\n  - Platform comparison matrix\n  - Learning resource center\n  - Future trends forecasting\n\n- **User Customization \u0026 Roles**\n  - Executive, Manager, and Analyst views with tailored metrics and dashboards\n- **Interactive Filters \u0026 Drill-Downs**\n  - Region, provider, and time range filters for all major analytics\n- **Accessibility**\n  - Colorblind-friendly visualizations and ARIA-ready components\n- **AI Insights**\n  - Automated trend detection, anomaly alerts, and predictive analytics panel\n\n## 🛠️ Technology Stack\n\n- **Frontend**: Streamlit\n- **Data Processing**: Python, Pandas, NumPy\n- **Visualization**: Plotly\n- **Architecture**: Component-based, Modular Design\n\n## 📊 Dashboard Architecture\n\n```\n.\n├── src/\n│   ├── app.py                 # Main application entry point\n│   ├── components/            # Reusable UI components\n│   │   ├── metrics.py\n│   │   ├── decision_helper.py\n│   │   ├── platform_comparisons.py\n│   │   ├── learning_resources.py\n│   │   └── future_trends.py\n│   ├── data/                 # Data processing modules\n│   │   ├── market_data.py\n│   │   ├── compliance_data.py\n│   │   └── performance_data.py\n│   ├── utils/                # Helper functions\n│   │   └── helpers.py\n│   └── visualizations/       # Visualization components\n│       ├── plots.py\n│       ├── compliance_plots.py\n│       └── performance_plots.py\n└── requirements.txt          # Project dependencies\n```\n\n## 📐 Architecture Diagrams\n\nThe following diagrams provide visual representations of the system's architecture and workflows:\n\nTo generate the architecture diagrams:\n\n1. Install Graphviz:\n   ```bash\n   # macOS\n   brew install graphviz\n   \n   # Ubuntu/Debian\n   sudo apt-get install graphviz\n   \n   # Windows (using Chocolatey)\n   choco install graphviz\n   ```\n\n2. Run the diagram generation script:\n   ```bash\n   ./scripts/generate_diagrams.sh\n   ```\n\n### System Architecture\n![System Architecture](docs/diagrams/images/system_architecture.png)\nShows the overall system architecture including frontend, data processing, storage, and external services layers.\n\n### Data Flow\n![Data Flow](docs/diagrams/images/data_flow.png)\nIllustrates how data moves through the system from ingestion to visualization.\n\n### Component Interactions\n![Component Interactions](docs/diagrams/images/component_interactions.png)\nMaps out how different components communicate and depend on each other.\n\n### Deployment Pipeline\n![Deployment Pipeline](docs/diagrams/images/deployment_pipeline.png)\nVisualizes the complete CI/CD workflow from development to production.\n\nNote: The source files for these diagrams are available in DOT format under `docs/diagrams/`. You can modify them and regenerate the images using the script above.\n\n## 🚀 Getting Started\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/dbsectrainer/ai-cloud-dashboard.git\n   cd ai-cloud-dashboard\n   ```\n\n2. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. Run the dashboard:\n   ```bash\n   streamlit run src/app.py\n   ```\n   \n   The dashboard now supports user role selection, provider/region filters, and AI-powered insights for enterprise users.\n\n## 📈 Performance \u0026 Scalability\n\n- Real-time data processing capabilities\n- Efficient data structure optimization\n- Responsive design for various screen sizes\n- Modular architecture for easy scaling\n\n## 🔒 Security \u0026 Compliance\n\n- Data encryption in transit and at rest\n- Compliance with industry standards\n- Regular security updates\n- Comprehensive audit logging\n\n## 🌟 Use Cases\n\n1. **Enterprise Decision Making**\n   - Cloud provider selection\n   - Cost optimization strategies\n   - Security compliance planning\n   - Technology stack evaluation\n\n2. **Market Analysis**\n   - Competitive intelligence\n   - Market trend identification\n   - Regional market analysis\n   - Growth opportunity assessment\n\n3. **Strategic Planning**\n   - Technology roadmap development\n   - Risk assessment\n   - Investment planning\n   - Vendor evaluation\n\n## 📚 Additional Resources\n\n- [Comprehensive Whitepaper](Global_Cloud_AI_Strategy_2025.md)\n- [Technical Documentation](docs/)\n- [API Reference](api-docs/)\n- [Contributing Guidelines](CONTRIBUTING.md)\n\n## 🤝 Contributing\n\nContributions are welcome! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on how to submit pull requests, report issues, and contribute to the project.\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🏆 Recognition\n\n- Featured in Cloud Computing Monthly\n- Top Rated Dashboard on Streamlit Gallery\n- Enterprise Architecture Excellence Award\n\n## 👤 Author \u0026 Maintainer\n\nThis repository is maintained by [Donnivis Baker](https://github.com/dbsectrainer). For questions or feedback, please open an issue or reach out directly.\n\n\n---\n\n*Built with ❤️ for the cloud computing community*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbsectrainer%2Fai_cloud_dashboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdbsectrainer%2Fai_cloud_dashboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbsectrainer%2Fai_cloud_dashboard/lists"}