A cloud-native analytics platform focused on the Mexican Data Center market, built with Data Engineering and Infrastructure-as-Code principles.
- Cloud Infrastructure: Automated deployment to AWS ECS Fargate using Terraform.
- Custom Networking: Implements a dedicated VPC architecture with subnet isolation and an Application Load Balancer (ALB) for secure, scalable delivery.
- Data Engineering Standards: Decoupled compute from storage. Uses a modular seeding pipeline (
scripts/seed_data.py) to hydrate the environment during the Docker build process. - Compliance-First Design: Analytics and energy calculators are strictly derived from TIA-942-C and ISO 27001 standards.
- Dashboard: Streamlit (Python)
- Infrastructure: Terraform
- Cloud: AWS (ECR, ECS, VPC, ALB, IAM)
- Containerization: Docker
- Data Processing: Pandas
The project is fully containerized. To run the dashboard locally:
# Build the image (runs the data seeding pipeline automatically)
docker build -t dc-dashboard .
# Run the container
docker run -p 8501:8501 dc-dashboardThe dashboard will be available at http://localhost:8501.
├── infra/ # Terraform configurations (VPC, ECS, Security Groups)
├── scripts/ # Data ingestion and seeding pipelines
├── pages/ # Dashboard modules (Operations, Energy, Market, etc.)
├── utils/ # Shared logic, data loaders, and theme config
├── data/ # Local data lake (Ignored by Git, managed by scripts)
├── Dockerfile # Multi-step build with automated data hydration
└── app.py # Main entry point
All metrics and projections are traceable to the following primary sources:
- MEXDC & Consumotic Report 2026: Market capacity and regional hub projections.
- TIA-942-C Standard: Rack density limits and liquid cooling thresholds.
- Uptime Institute (2024): Global PUE benchmarks and workload trends.
- ISO/IEC 27001: Physical and logical security control checklists.
Developed for the Data Engineering & AI career path (UPY).