Flight Insight

A Real-Time Flight Data Engineering Pipeline

Collect and display history of flight arrival data using Aviation-Edge API

An end-to-end data engineering project.

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🎯 Project Goal (High Level)

Build a Data Pipeline to Collect, Store, Process, and Visualize Real-Time Flight Data

You will:

• Use modern, production-ready tools
• Create a repeatable, automated data pipeline
• Include monitoring, testing, and documentation
• Deliver a mini dashboard or API

🔍 Use Case:

Collect global flight data (e.g. departures, arrivals, routes, delays), process it, and store it for trend analysis, alerts (like abnormal delays), or dashboards.

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🧱 Tech Stack

Layer	Tools/Tech
Ingestion	Python, Requests, Airflow (Scheduler)
Orchestration	Apache Airflow (DAGs to automate ETL)
Storage	PostgreSQL (raw + cleaned data)
Processing	dbt (for SQL-based transformation)
Containerization	Docker (run Airflow, PostgreSQL, etc.)
Monitoring	Airflow UI, logging, optional: Prometheus/Grafana
Testing	Pytest + dbt tests
Visualization	Streamlit or simple Flask dashboard
CI/CD (Bonus)	GitHub Actions
Infra (Bonus)	Terraform (if deploying on cloud)

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🗂️ Project Structure

flight_insight/
├── dags/                   ← Airflow DAGs
├── data/                   ← Local storage (if any)
├── dbt/                    ← dbt models for transformation
├── scripts/                ← Custom Python scripts
│   └── extract_flight_data.py
├── docker/                 ← Docker configs
│   └── docker-compose.yml
├── airflow/                ← Airflow-specific config (plugins, etc.)
├── notebooks/              ← EDA, quick queries
├── tests/                  ← Unit and integration tests
├── README.md
├── requirements.txt
└── .env

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📈 Possible Features

🔹 Basic MVP

• Connect to aviation-edge API (e.g., departures, delays)
• Store raw data in PostgreSQL
• Schedule ingestion daily/hourly using Airflow
• Transform data using dbt (normalize, calculate delay stats)
• Dashboard to view delay trends per airport

🔹 Advanced Add-Ons

• Deduplicate and validate using dbt tests
• Detect anomalies (e.g. delay spike) using a basic ML model or SQL rule
• Streamlit dashboard showing top delayed routes, airlines, etc.
• Alerts (Slack/email) on specific triggers (e.g. >3hr delay)
• Archive old data to S3 (mock or real)
• Expose cleaned data via REST API (Flask/FastAPI)

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✅ This Project Shows Off:

• Modern ETL design (modular, scalable)
• Hands-on with orchestration (Airflow)
• Real-world data processing (messy API data)
• Writing clean, testable Python code
• Deployment and automation skills (Docker, Airflow)
• Data modeling and transformation (dbt)
• Optional: basic visualization (not just backend)

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