https://github.com/yousef-jaber-abdelaziz/electrical-vehicles-data-analysis-project

A full stack Data Engineering f\project from Getting the data to the Data warehousing and then the Dashboard using Power BI
https://github.com/yousef-jaber-abdelaziz/electrical-vehicles-data-analysis-project

data-analysis data-engineering data-modeling data-visualization data-warehouse data-warehousing fabric microsoft-azure microsoft-fabric-data-engineer powerbi sql-server

Last synced: about 1 month ago
JSON representation

A full stack Data Engineering f\project from Getting the data to the Data warehousing and then the Dashboard using Power BI

• Host: GitHub
• URL: https://github.com/yousef-jaber-abdelaziz/electrical-vehicles-data-analysis-project
• Owner: Yousef-Jaber-Abdelaziz
• Created: 2025-09-09T22:07:27.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-09-09T23:24:04.000Z (about 1 month ago)
• Last Synced: 2025-09-10T02:02:55.831Z (about 1 month ago)
• Topics: data-analysis, data-engineering, data-modeling, data-visualization, data-warehouse, data-warehousing, fabric, microsoft-azure, microsoft-fabric-data-engineer, powerbi, sql-server
• Homepage:
• Size: 8.09 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# ⚡ Electrical Vehicles Analysis – Cloud Data Engineering Project  

## 📌 Project Overview  

The Electrical Vehicles Analysis project is a cloud-based data engineering solution designed to analyze electric vehicle datasets and deliver insights into battery technology, performance, pricing, and sales trends. Using **Microsoft Fabric (Dataflows, Lakehouse, Data Pipelines, Warehouse)**, **SQL**, and **Power BI**, this project demonstrates how raw EV data can be transformed into a robust data warehouse and semantic layer for business analytics.  

---

## 🎯 Objectives  

- Build a **cloud data warehouse** for analyzing EV technical and market data.  

- Design and implement an **ETL pipeline** using Microsoft Fabric (Bronze → Silver → Gold).  

- Create a **semantic model directly in the Data Warehouse**.  

- Visualize data through an **interactive Power BI dashboard**.  

---

## 🛠️ Tools & Technologies  

- **Microsoft Fabric (Lakehouse, Dataflows, Pipelines, Warehouse)** → ETL, storage & orchestration  

- **SQL** → Data transformation & modeling  

- **Power BI** → Dashboarding & reporting  

---

## 📊 Data Warehouse Design  

- **Fact Table:**  

  - `VehicleFact` → Holds core vehicle measures (battery capacity, range, price, units sold, warranty, etc.)  

- **Dimension Tables:**  

  - `YearDim` → Model year & decade context  

  - `BatteryDim` → Battery & charging attributes  

  - `VehicleModelDim` → Manufacturer & model details  

  - `CountryColorDim` → Country of manufacture & color options  

  

    

      Conceptual Model


      

    

    

      Logical Model


      

    

  

  

---

## 🔄 Fabric ETL Pipeline  

1. **Bronze Layer (ODS):**  

   - Created a Lakehouse and uploaded the raw CSV dataset.  

2. **Silver Layer (STG):**  

   - Converted raw CSV into structured tables.  

   - Created **EV Silver Dataflow** for initial cleaning & structuring.  

3. **Gold Layer (DWH):**  

   - Built **EV Dimensions Gold Dataflow** to load cleaned dimension tables into the Warehouse.  

   - Built **EV Gold Fact Dataflow** to load fact measures (battery capacity, range, sales, etc.) and join with dimensions.  

   - Configured **indexes** for optimized querying.  

4. **Pipeline Orchestration:**  

   - Designed a **Fabric Data Pipeline** to connect all dataflows, enabling automated end-to-end data loading.  

---

## 📐 Semantic Model  

- Created relationships directly in the **Fabric Data Warehouse** between `VehicleFact` and its dimensions.  

- Defined **business-friendly measures (DAX)** for KPIs such as:  

```DAX

-- Number of Car Models

Car Models = DISTINCTCOUNT(VehicleFact[VehicleModelID])

-- Number of Manufacturers

Manufacturers = DISTINCTCOUNT(VehicleModelDim[Manufacturer])

-- Average Battery Capacity

Avg Battery Capacity = AVERAGE(VehicleFact[BatteryCapacityKwh])

-- Average Range

Avg Range = AVERAGE(VehicleFact[RangeKM])

-- Total Units Sold

Total Units Sold = SUM(VehicleFact[UnitsSold2024])

-- Units Sold Value

Units Sold Price = SUMX(VehicleFact, VehicleFact[PriceUSD] * VehicleFact[UnitsSold2024])

```  

---

## 📈 Power BI Dashboards  

The Power BI dashboard provides insights across multiple views:  

- **Overview Page** → Historical EV overview, models, units sold, total revenue.  

- **EV Sales Analysis** → Units sold by year, country, and manufacturer.  

- **Battery Analysis** → Distribution of battery capacities & charging types.  

- **Navigation Page** → Easy page switching & filtering.  

  

    

      Dashboard Home Page


      

    

    

      Overview Page


      

    

    

      Sales Analysis


      

    

  

  

    

      Battery Analysis


      

    

  

  

---

## 🙏 Acknowledgment  

Thank you for reviewing this project. This work was completed as part of my professional training and demonstrates the use of **data engineering & BI practices** to analyze the growing **electrical vehicle industry**.  

## 📂 Project Documentation