High-Throughput Fleet Telematics Engine
Client Partner: SwiftCargo Logistics · Build Timeline: 7 Months
Project Tech Stack
Allocated Engineering Pod
- Backend Engineersx3
- Data Engineersx2
- Project Managerx1
"The telematics platform has changed how we manage fleet operations. We now predict delay patterns and save thousands on fuel costs."
Carlos Santana
VP of Telematics, SwiftCargo Logistics
1. Context & Business Goal
The Core Challenge: SwiftCargo's trackers sent GPS coordinates every 10 seconds across 12,000 trucks, overload their servers and causing delays in delivery tracking.
The Agreed Objective: Build an event-streaming backend to process sensor telemetry in real-time and provide updates on vehicle health and routes.
2. Technical Research & Discovery
Analyzed sensor payloads to compress JSON data and improve database insert speeds. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: Map dashboard showing active trucks, with clickable vehicle details and live speed/temperature meters.
Database Infrastructure Setup: Go-based ingestion layer backed by Kafka, loading telemetry into a TimescaleDB database with Redis caches.
4. Production Implementation Lifecycle
Ingestion Layer Setup
Built a high-speed Go microservice to process incoming HTTP coordinates.
Kafka Event Queues
Structured Kafka brokers to scale incoming telemetry data streams.
Database Index Tuning
Optimized TimescaleDB partition tables to speed up coordinates queries.
5. Validated Business Outcomes
- ✓Processes 45 million daily telemetry pings without database crashes.
- ✓Provides dispatchers with live truck routes and automated delay alerts.
- ✓Helped reduce fuel consumption by 14% using route optimization analytics.