https://github.com/dbsectrainer/ai_cloud_dashboard
https://github.com/dbsectrainer/ai_cloud_dashboard
domain-cloud lifecycle-maintenance tech-python type-edu
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/dbsectrainer/ai_cloud_dashboard
- Owner: dbsectrainer
- License: mit
- Created: 2025-01-30T00:37:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-18T00:48:38.000Z (7 months ago)
- Last Synced: 2025-11-18T02:24:35.605Z (7 months ago)
- Topics: domain-cloud, lifecycle-maintenance, tech-python, type-edu
- Language: Python
- Size: 973 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: docs/security.md
Awesome Lists containing this project
README
# Global AI & Cloud Intelligence Dashboard 🌐
A 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.
## 🚀 Key Features
- **Market Intelligence**
- Real-time market share analysis
- Growth trend visualization
- Regional market dynamics
- Competitive landscape analysis
- **Security & Compliance**
- Compliance requirement tracking
- Security score monitoring
- Certification timeline management
- Data residency visualization
- **Cost Analysis**
- Total Cost of Ownership (TCO) calculator
- Provider cost comparisons
- Budget optimization tools
- Resource utilization tracking
- **Performance Metrics**
- Real-time performance monitoring
- Global latency analysis
- SLA compliance tracking
- Resource efficiency metrics
- **Strategic Tools**
- AI-powered decision support
- Platform comparison matrix
- Learning resource center
- Future trends forecasting
- **User Customization & Roles**
- Executive, Manager, and Analyst views with tailored metrics and dashboards
- **Interactive Filters & Drill-Downs**
- Region, provider, and time range filters for all major analytics
- **Accessibility**
- Colorblind-friendly visualizations and ARIA-ready components
- **AI Insights**
- Automated trend detection, anomaly alerts, and predictive analytics panel
## 🛠️ Technology Stack
- **Frontend**: Streamlit
- **Data Processing**: Python, Pandas, NumPy
- **Visualization**: Plotly
- **Architecture**: Component-based, Modular Design
## 📊 Dashboard Architecture
```
.
├── src/
│ ├── app.py # Main application entry point
│ ├── components/ # Reusable UI components
│ │ ├── metrics.py
│ │ ├── decision_helper.py
│ │ ├── platform_comparisons.py
│ │ ├── learning_resources.py
│ │ └── future_trends.py
│ ├── data/ # Data processing modules
│ │ ├── market_data.py
│ │ ├── compliance_data.py
│ │ └── performance_data.py
│ ├── utils/ # Helper functions
│ │ └── helpers.py
│ └── visualizations/ # Visualization components
│ ├── plots.py
│ ├── compliance_plots.py
│ └── performance_plots.py
└── requirements.txt # Project dependencies
```
## 📐 Architecture Diagrams
The following diagrams provide visual representations of the system's architecture and workflows:
To generate the architecture diagrams:
1. Install Graphviz:
```bash
# macOS
brew install graphviz
# Ubuntu/Debian
sudo apt-get install graphviz
# Windows (using Chocolatey)
choco install graphviz
```
2. Run the diagram generation script:
```bash
./scripts/generate_diagrams.sh
```
### System Architecture
Shows the overall system architecture including frontend, data processing, storage, and external services layers.
### Data Flow
Illustrates how data moves through the system from ingestion to visualization.
### Component Interactions
Maps out how different components communicate and depend on each other.
### Deployment Pipeline
Visualizes the complete CI/CD workflow from development to production.
Note: 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.
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/dbsectrainer/ai-cloud-dashboard.git
cd ai-cloud-dashboard
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the dashboard:
```bash
streamlit run src/app.py
```
The dashboard now supports user role selection, provider/region filters, and AI-powered insights for enterprise users.
## 📈 Performance & Scalability
- Real-time data processing capabilities
- Efficient data structure optimization
- Responsive design for various screen sizes
- Modular architecture for easy scaling
## 🔒 Security & Compliance
- Data encryption in transit and at rest
- Compliance with industry standards
- Regular security updates
- Comprehensive audit logging
## 🌟 Use Cases
1. **Enterprise Decision Making**
- Cloud provider selection
- Cost optimization strategies
- Security compliance planning
- Technology stack evaluation
2. **Market Analysis**
- Competitive intelligence
- Market trend identification
- Regional market analysis
- Growth opportunity assessment
3. **Strategic Planning**
- Technology roadmap development
- Risk assessment
- Investment planning
- Vendor evaluation
## 📚 Additional Resources
- [Comprehensive Whitepaper](Global_Cloud_AI_Strategy_2025.md)
- [Technical Documentation](docs/)
- [API Reference](api-docs/)
- [Contributing Guidelines](CONTRIBUTING.md)
## 🤝 Contributing
Contributions are welcome! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on how to submit pull requests, report issues, and contribute to the project.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🏆 Recognition
- Featured in Cloud Computing Monthly
- Top Rated Dashboard on Streamlit Gallery
- Enterprise Architecture Excellence Award
## 👤 Author & Maintainer
This repository is maintained by [Donnivis Baker](https://github.com/dbsectrainer). For questions or feedback, please open an issue or reach out directly.
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*Built with ❤️ for the cloud computing community*