HealthSense – End-to-End Health Data Analytics Platform Stack: Python | Apache Airflow | FastAPI | PostgreSQL | Docker | Kubernetes | GCP | Terraform Developed a scalable health data analytics platform that fetches, transforms, and stores real-time public health data (e.g., COVID-19 stats from WHO API). Built automated ETL pipelines using Apache Airflow, exposed data services via FastAPI, and containerized the entire stack with Docker. Orchestrated deployments via Kubernetes and infrastructure as code with Terraform on Google Cloud Platform. Implemented monitoring using Prometheus and enabled CORS for seamless frontend-backend integration. Key Points: • Automated ingestion and transformation of large-scale health data • Built modular and reproducible ETL pipelines using Apache Airflow DAGs • Designed RESTful APIs for health data access using FastAPI • Deployed containerized services on GCP Kubernetes Engine • Managed infrastructure using Terraform with cost-aware resource provisioning • Enabled API monitoring and metrics collection using Prometheus • Followed best practices in data engineering, cloud DevOps, and API design Outcome: The HealthSense platform is a basic Analytics Dashboard. That is mainly built to make use of all the available Infra. This is aimed as a learning challenge.