ReTrackLogistics is a SaaS-style Django backend that models a freight visibility platform, built with a Data Engineering–first perspective.
It’s a sandbox for combining backend architecture (Django + DRF), geospatial and real-time systems, event-driven workflows, and multi-tenant access control with data engineering concepts like CDC, streaming pipelines, and analytical workflows — all within the context of predictive logistics and simulated IoT tracking.
ReTrackLogistics also lays the foundation for BackendToBytes.org — a project-based learning platform focused on bridging Backend Development and modern Data Engineering.
High-level outcomes ReTrackLogistics enables for logistics teams:
- Track shipments in real time
- Manage carriers, drivers, and vehicles
- Detect arrival and departure milestones automatically
- Record delivery events and flag anomalies
- Predict ETAs based on live tracking data
- Visualize the full shipment lifecycle
Built for developers, with flexibility, modularity, and modern tooling in mind:
- ✅ Modular Django App Design — Clear separation of concerns across shipments, carriers, events, and devices
- ✅ Extensible REST API — Built with Django REST Framework for easy integration and extension
- ✅ Simulated Data Generation — Python-based mock loaders and schedulers for development and testing
- ✅ Role-Based Access Control — Fine-grained permissions to support real-world logistics workflows
- ✅ Containerized & Cloud-Ready — Dockerized with support for AWS ECS, environment configs, and future CI/CD pipelines
The architecture behind ReTrackLogistics supports real-world data engineering use cases — from IoT event capture to pipeline orchestration and analytics workflows.
It’s designed around the following core principles:
- Real-Time Event Capture — High-throughput ingestion of simulated GPS and device events, modeled after real-time streams from distributed sources
- Device Activity Modeling — Standardized tracking of asset status and events, designed to work across different use cases and industries
- Geospatial & Time-Based Data Management — Efficient handling of location and time-based data for both real-time operations and downstream analytics
- Cloud-Native Simulation — Mocking device behavior using Python scripts and scheduled AWS ECS tasks to test ingestion and processing at scale
- Modern Data Stack Integration — Supports tools like dbt, Snowflake, and AWS to build scalable, analytics-ready pipelines from IoT event data
- Backend: Django + Django REST Framework
- Database: PostgreSQL
- Event Simulation: Python scripts + AWS ECS (scheduled tasks)
- Cloud: AWS ECS, S3
- Data Stack (Planned): dbt, Snowflake, Kafka/Kinesis, Airflow
- Testing & Dev Tools: Docker, pytest
This is a personal project for learning and experimentation. ReTrackLogistics is not affiliated with or endorsed by any logistics company.
Reach out or fork the repo — and stay tuned for BackendToBytes.org.