Alidzhon Aminov

Real-Time Fleet Telemetry Pipeline for Berlin Area: Synthetic Data Generation with Live and Historical Dashboards

Project Summary

In this project, I developed a complete end-to-end fleet monitoring system that generated synthetic vehicle telemetry, processed it through a real-time streaming pipeline, and visualized fleet operations on three interactive dashboards.

Highlights:

• Developed Python code for synthetic data generator that can produce 1000+ vehicle journeys with realistic GPS routes, sensor failures, and repair routing
• Built real-time streaming pipeline with Kafka, Redis, and MongoDB for data ingestion and storage.
• Created live dashboard using WebSocket to track vehicle locations and failures in real-time across Berlin area
• Designed historical dashboard for route analysis, failure timelines, and performance trends over custom date ranges
• Implemented full-stack system with Flask backend, JavaScript frontend

Outcome:

• Three-dashboard visualization system - Real-time monitoring for live operations + historical analysis for performance insights.
• Complete data pipeline - Generated → Streamed → Stored → Visualized in three dashboards.
• Production-ready architecture with scalable streaming infrastructure.