My Flight Tracking website is live! https://sdr-flight-map-webapp.azurewebsites.net/ After multiple iterations to get the device working without glitches, build the data pipeline, and finally deploy to the cloud, I’ve launched my own local flight tracking web app

Why I built this

I’ve always been intrigued by data from satellites, drones, and flights. Aircraft generate massive amounts of invisible radio signal data, but it’s rarely accessible in a way that feels local and relevant. At the same time, flights contribute to air traffic, emissions, and resource consumption, yet the data behind these systems often feels abstract.

I wanted to answer two questions:

1. How do we access and decode these signals?
2. How can we make this invisible stream of data more understandable and interactive for the public—especially on a local scale (about a 40-mile radius around me)?

How I Built It

• Hardware Integration – Used an SDR (Software Defined Radio) USB antenna device from Amazon to capture ADS-B signals broadcast by airplanes.
• Data Decoding – Configured dump1090 on Ubuntu (Linux) to read live data from the SDR antenna.
• Data Pipeline – Wrote a Python script to process signals and store them in a cloud database.
• Database – Leveraged Azure SQL Database (Free Tier)https://learn.microsoft.com/en-us/azure/azure-sql/database/free-sql-db for persistent and scalable storage.
• Backend API – Built a FastAPI service in Python to serve flight data to clients. Deployed via Azure App Service (Free Tier)https://azure.microsoft.com/en-us/pricing/free/services/app-service/.
• Frontend Visualization – Built a React web application using Leaflet for interactive mapping and hosted it on Azure App Service.

Pipeline in one line

• SDR Antenna → dump1090 on local ubuntu machine → Python script → Azure SQL Database → FastAPI (Python) to read data from Azure SQL Database → React Web App (Leaflet)

What I Learned

• How to create a signal-to-visualization pipeline from SDR hardware to a cloud-hosted web app.
• Implementing full stack near real-time data flows with hardware, backend, and frontend.
• Setting up a Linux-based SDR environment (Ubuntu) for maximum compatibility.
• Designing cloud-based, scalable architecture using free tiers of Azure services.
• Deploying full-stack apps that integrate hardware + cloud + frontend.
• Configuring and querying a Azure SQL cloud database for persistent storage.
• Using Leaflet for geospatial visualization.

Impact

• Made invisible air traffic visible, interactive, and public.
• Combined data engineering, backend APIs, frontend visualization, and cloud deployment into one project.
• Showed how cloud platforms like Azure can democratize access to real-time, location-specific data.
• Created a tool that makes global air traffic understandable at a local level—educational for the public, and a showcase of cloud + hardware integration.
• Demonstrated skills in hardware integration, cloud deployment, and near real-time data handling.