A lightweight, production-style workflow that illustrates how modern IoT and telematics platforms ingest, validate, process, and visualize real-world sensor data. Built to show how raw signals become operational insight.
Overview
This demo walks through a miniature connected-operations pipeline—from sensor → API → processing → dashboard.
A simulated OBD-II data stream (RPM, coolant temperature, speed, etc.) is pushed into a FastAPI ingestion service, validated, stored as time-series data, and surfaced through a Streamlit dashboard for live visualization.
The architecture mirrors patterns used across fleet, equipment monitoring, and connected-operations platforms:
High-volume telemetry ingestion
REST API validation and business rules
Time-series processing and anomaly visibility
Clear communication of system behavior in a demo-ready format
The goal: demonstrate how operational visibility becomes predictability—a core value proposition for any predictive maintenance or telematics product.
How It Works
Sensor Emulator (Python)
Generates realistic Corolla OBD-II sensor signals, following natural engine-state behavior.
This models how actual telematics devices deliver data from the field.
API Layer (FastAPI)
Receives incoming telemetry
Validates payload shape and schema
Applies basic business logic (range checks, state transitions)
Stores clean samples into a time-series datastore
This layer demonstrates ingestion reliability, structured processing, and API fluency—core SE competencies when explaining platform behavior to customers.
Dashboard (Streamlit)
Displays real-time RPM trends, recent values, and raw JSON telemetry.
This ties the end-to-end workflow together and supports clear technical storytelling during demos.
Tech Stack & Business Value
Tech:
Python, FastAPI, Streamlit, Matplotlib, JSON time-series storage, Docker (optional multi-service setup)
Why This Project Matters in a Sales Engineering Context:
Hiring teams want to see that an SE can explain:
How data flows from device → cloud → insight
Why ingestion and validation matter for reliability and safety
How raw telemetry becomes business intelligence
Where failure points occur in real pipelines (latency, ingestion drops, schema drift)
How technical choices mitigate operational risk
This project is a concrete, demo-ready example of an end-to-end connected-operations workflow—a perfect match for telematics, IoT, observability, and equipment-monitoring SaaS products.
