⚡ 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.

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🎯 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.

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🛠️ Tools & Technologies

• Microsoft Fabric (Lakehouse, Dataflows, Pipelines, Warehouse) → ETL, storage & orchestration
• SQL → Data transformation & modeling
• Power BI → Dashboarding & reporting

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📊 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

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🔄 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.

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📐 Semantic Model

• Created relationships directly in the Fabric Data Warehouse between VehicleFact and its dimensions.
• Defined business-friendly measures (DAX) for KPIs such as:

-- 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])

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📈 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

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🙏 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