MuseMotion is a data engineering platform built to process, analyze, and visualize insights from electric vehicle (EV) datasets. It automates data ingestion, transformation, and cloud-based storage using Python-powered ETL pipelines and advanced SQL queries. By transforming raw EV data into structured intelligence, MuseMotion bridges data engineering and sustainability—helping teams extract meaningful insights that drive innovation in the electric mobility space.
MuseMotion was built to demonstrate how data engineers can translate large, messy datasets into reliable and insightful metrics—tracking battery performance, charging trends, and EV efficiency at scale.
Our goal was to create a working prototype that integrates SQL, Python, and Azure cloud services to simulate a professional-grade ETL process, building the bridge between data insight and actionable ignition in the EV ecosystem.
- Automated ETL Pipeline: End-to-end Extract, Transform, and Load process using Python. SQL Analysis: Advanced queries (joins, aggregations, subqueries, CRUD operations) for EV data insights.
- Cloud Integration (Azure): Uploads both raw and processed data to Azure Blob Storage and connects to Azure SQL Database.
- Data Quality Checks: Cleans, validates, and logs data transformations for accuracy and consistency.
- Visualization Dashboard: Streamlit dashboard for viewing EV insights and monitoring pipeline performance.
- Scalable Design: Supports local SQLite testing and cloud deployment for production-ready workflows.
Database Analysis:
- Kaggle Datasets for dataset sourcing.
- SQL for data querying and manipulation.
- MySQL Database.
- Pandas analysis.
Cloud Platform:
- Microsoft Azure.
- Streamlit for Dashboard creation.
Link: https://week4-musemotion-7rxfcytyna5vt9batbjv3q.streamlit.app/
- Long-Term Tracking: Extend data collection to analyze EV performance over time, not just single snapshots.
- Predictive Analytics: Integrate models to forecast EV demand and charging patterns.
- Enhanced Visualization: Expand Streamlit dashboards for deeper insights.
- Automated Notifications: Use Azure Logic Apps to send alerts for failed uploads or pipeline errors.
- Full Azure Integration: Transition from SQLite to fully cloud-hosted Azure SQL workflows.
This project was created as part of a coding bootcamp group's Data Pipeline Builder
| Member | Role | Responsibilities |
|---|---|---|
| Aobakwe Modillane | Scrum Master. | Project management, repository setup, dashboard development, cloud integration, documentation. |
| Boikanyo Maswi | Junior Developer. | SQL scripts, ETL logic, Streamlit dashboard, README & repo documentation, README.md, repo about. |
| Luyanda Zuma | Junior Develper. | SQL scripts, ETL logic, Streamlit dashboard, README & repo documentation. |
| Nqobile Masombuka | Junior Developer. | Excel data cleaning, documentation, README.md. |