OBD-II Telemetry Emulator - End to end Connected Operations Demo
OBD-II Telemetry Emulator - End to end Connected Operations Demo
OBD-II Telemetry Emulator - End to end Connected Operations Demo
A lightweight, end-to-end workflow that models how IoT and telematics platforms ingest, validate, and visualize real-world sensor data.
A lightweight, end-to-end workflow that models how IoT and telematics platforms ingest, validate, and visualize real-world sensor data.
Overview
This demo walks through a miniature connected-operations pipeline from sensor → API → dashboard. It generates simulated OBD-II telemetry (RPM, coolant temp, speed, etc.) and streams it into a FastAPI ingestion service, which then feeds a Streamlit dashboard for simple trend visualization.
The build mirrors patterns used across IoT, fleet tech, and equipment-monitoring solutions: data generation, REST ingestion, schema validation, time-series handling, and clear communication of system behavior.
How It Works
Emulator (Python):
Produces realistic Corolla OBD-II sensor signals, following engine-state patterns to mimic how actual telematics devices deliver data in the field.
API Layer (FastAPI):
Receives incoming telemetry, validates JSON payloads, applies basic business rules, and stores time-series entries. Demonstrates ingestion reliability and structured processing.
Dashboard (Streamlit):
Displays real-time RPM patterns, recent values, and raw JSON—completing the end-to-end workflow and supporting technical storytelling in demos.
Tech Stack & Business Value
Tech:
Python, FastAPI, Streamlit, Matplotlib, JSON time-series storage.
Why This Project Matters in a Sales Engineering Context:
This project models an end-to-end connected-operations pipeline: sensor → API ingestion → validation → time-series storage → insight. It mirrors the exact workflow used by IoT, fleet, equipment-monitoring, and connected-systems SaaS products, making it a perfect SE demo asset—not just a tech exercise.
For hiring teams, it demonstrates:
Ability to explain data flow clearly at a level customers actually understand
Awareness of how IoT platforms convert noisy sensor data into business intelligence
Skill in stitching together multiple components into a clean, customer-ready narrative
Familiarity with failure points (bad data, latency, ingestion issues) an SE must anticipate
Proof you can present a “day-in-the-life” workflow: This is how raw signals become insight.
Technical Sales Engineer Portfolio
