Inspired by the architectural challenges of large-scale systems like Uber, I designed and implemented an end-to-end vehicle and object tracking platform. The goal was to build a robust, scalable, and real-time system capable of ingesting and processing high-velocity data streams from numerous IoT devices.

The platform is built on an event-driven, microservices architecture to ensure loose coupling and independent scalability of its components.

Core Architectural Components

• Data Ingestion: A lightweight REST API serves as the ingestion point, forwarding incoming device data to an Apache Kafka cluster. Kafka acts as the central, durable message bus, decoupling data producers from consumers and ensuring data resilience.

• Real-Time Stream Processing: Apache Flink is used for the core stream processing logic. It consumes data from Kafka topics in real-time to perform complex event processing, such as data enrichment, trip detection, and geofence analysis. Redis is utilized for fast, distributed state management within the Flink jobs.

• Data Persistence: Processed and enriched location data is persisted in a PostgreSQL database supercharged with the TimescaleDB and PostGIS extensions. This combination provides a highly efficient solution for storing, indexing, and querying vast amounts of time-series and geospatial data.

• Backend & Real-Time Communication: A suite of backend services, developed with Node.js, exposes the processed data through a REST API for client applications. For live, real-time tracking on a user interface, a WebSocket server pushes location updates directly to connected clients.

The entire system is containerized using Docker, with a docker-compose.yml file defining and managing the multi-container application environment for seamless development and deployment.

tech stack / keywords

• System Design
• Event-Driven Architecture
• Microservices
• Apache Kafka
• Apache Flink
• TimescaleDB / PostGIS
• Redis
• Node.js (Express, WebSocket)
• Docker