🚀 Building a Real-Time Flight Anomaly Detection System (End-to-End).

From Streaming Data to Live Dashboard using Kafka, FastAPI, PostgreSQL & Valkey

🌍 How This All Started (Aiven + Inspiration)

A few days ago, while scrolling through social media, I came across something exciting…

👉 A challenge by Aiven — the #AivenFreeTier competition.

And honestly… it immediately caught my attention.

Why?

Because they are offering something that every developer dreams of:

💡 Real cloud infrastructure — completely FREE — with no credit card, no trial expiration.

⚡ What Makes Aiven So Exciting?

At Aiven, the philosophy is simple:

“The best way to learn is by building real systems.”

And they truly mean it.

🔥 Their Free Tier Includes:

  • Apache Kafka → Real-time data streaming
  • PostgreSQL → Powerful, production-grade database
  • MySQL → Classic web database
  • Valkey → Ultra-fast caching (Redis alternative)

✨ And the best part:

  • No credit card
  • No expiry
  • Fully managed
  • Ready for real-world projects

🏆 The Challenge That Hooked Me

The competition is simple but powerful:

  • Build something cool using Aiven services
  • Share your journey publicly
  • Tell your story (not just code!)
  • Use #AivenFreeTier

And…

💰 Win $1000 for the most compelling project

✨ Why I Built This Project

Instead of just reading about distributed systems, I asked myself:

🤔 “Can I actually build a real-time system like companies use in production?”

That curiosity led me to build something ambitious:

✈️ A Real-Time Flight Anomaly Detection System

🎯 The Problem

In aviation systems:

  • Aircraft continuously send telemetry data
  • Engineers must monitor everything in real-time
  • Failures must be detected instantly

❌ Challenges:

  • High-volume streaming data
  • Real-time processing required
  • No data loss allowed
  • Dashboard must always stay in sync

💡 My Solution

I built an event-driven, real-time system powered by Aiven cloud services.

✔️ What it does:

  • Generates synthetic flight telemetry data
  • Streams data using Kafka
  • Detects anomalies instantly
  • Stores history in PostgreSQL
  • Caches live data using Valkey
  • Displays everything on a real-time dashboard

⚠️ Note: This uses synthetic/sample data, not real aircraft data.

🧠 Architecture Overview



🔄 End-to-End Flow

  1. FastAPI generates flight data
  2. Data is sent to Kafka
  3. Consumer processes events
  4. Anomalies detected
  5. Stored in PostgreSQL
  6. Cached in Valkey
  7. WebSocket pushes updates
  8. Dashboard updates instantly

📸 Screenshots

🖥️ Real-Time Dashboard



Live flight tracking, alerts, and graphs

☁️ Aiven Cloud Services



Managed Kafka, PostgreSQL & Valkey

📡 Kafka Streaming



Real-time event flow

⚙️ Tech Stack

  • FastAPI → Backend
  • Apache Kafka → Streaming
  • PostgreSQL → Storage
  • Valkey → Cache
  • Chart.js → Visualization
  • Aiven → Managed Cloud

🔥 Why Aiven Made This Easy

Without Aiven, setting up all this would mean:

❌ Manual Kafka setup
❌ Database provisioning
❌ Scaling issues
❌ DevOps overhead

But with Aiven:

✅ Ready-to-use services
✅ Secure connections
✅ Zero setup complexity
✅ Focus on building, not infrastructure

🔥 Key Features

✅ Real-Time Streaming

Powered by Kafka

✅ Live Dashboard

WebSocket-based instant updates

✅ Fault Tolerance

  • Auto reconnect
  • Data recovery
  • No refresh loss

✅ Smart Visualization

  • Continuous graphs
  • Stable colors
  • Smooth rendering

✅ Multi-Alert System

Tracks multiple anomalies simultaneously

🧩 Core Logic Example

Risk Calculation

def calculate_risk(data):
    score = 0
    if data["altitude"] < 25000:
        score += 30
    if data["speed"] > 850:
        score += 25
    if data["engine_status"] == "FAIL":
        score += 50
    if data["fuel_level"] < 20:
        score += 20
    return min(score, 100)

📊 Why This Feels Like a Real System

This is not just a demo:

  • Event-driven architecture
  • Real-time streaming
  • Fault-tolerant design
  • Cloud-native services
  • Scalable approach

📁 Project Structure

flight-anomaly-system/
│
├── src/
│ ├── api/
│ │ ├── main.py              # FastAPI + WebSocket + generator
│ │ └── kafka_producer.py
│ │
│ ├── consumer/
│ │ └── kafka_consumer.py   # Anomaly detection
│ │
│ ├── db/
│ │ └── db.py
│ │
│ ├── cache/
│ │ └── cache.py
│ │
│ └── dashboard/
│ │  └── index.html         # Real-time UI
│ │
│ └── certs/
│ │ ├── kafka/
│ │ └── postgres/
│ │
│ ├── images/
│   └── aeropulse.png
│  
├── .env
├── requirements.txt
└── README.md

🚀 How to Run

uvicorn src.api.main:app --reload
python -m src.consumer.kafka_consumer

🔗 GitHub

👉 repo:

https://github.com/obulareddynew/flight-anomaly-system.git

🏆 What I Learned

  • Real-time system design
  • Kafka pipelines
  • WebSocket architecture
  • Cloud service integration
  • Debugging production issues

🚀 Future Plans

  • ML anomaly detection
  • Replay system
  • Kubernetes deployment
  • Observability tools

💬 Final Thoughts

This project started with:

👉 A simple scroll on social media
👉 A spark from Aiven
👉 And a curiosity to build something real

And it turned into:

💥 A complete real-time system built on cloud infrastructure

🎯 My Goal

  • Master real-time systems
  • Build production-grade projects
  • And yes…

🏆 Win the #AivenFreeTier challenge

🙌 Let’s Connect

If you found this interesting:

  • ⭐ Star the repo
  • 💬 Share feedback
  • 🚀 Build your own version

✨ “Learning by building real systems is the fastest way to grow — and Aiven made that possible.”


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