🏅 Olympic Data Engineering & Analytics Project

A complete data engineering pipeline and dashboarding project using Azure, Databricks, and Power BI. This project processes historical Olympic data using a Bronze-Silver-Gold architecture and presents insights with interactive dashboards.

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🚀 Project Overview

This project was completed as part of a data engineering internship and demonstrates how to:

• Ingest and store data using Azure Blob Storage
• Process large volumes of structured data using Databricks with PySpark
• Build a multi-layered architecture (Bronze → Silver → Gold)
• Create and share interactive dashboards with Power BI

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📁 Folder Structure

Olympic-DataEngineering-Project/ ├── datasets/ # Raw .csv files (optional or sample data) ├── images/ # Dashboard screenshots │ ├── Olympic dashboard page1.png │ └── Olympic Dashboard Page 2.png ├── O_Bronze.py # Bronze Layer script ├── O_Silver.py # Silver Layer script ├── O_Gold.py # Gold Layer script ├── olympicpbi.pdf # Power BI Dashboard export (PDF) ├── olympicp dashboard.pbix # Power BI file ├── LICENSE └── README.md

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⚙️ Tools & Technologies

Tool/Service	Purpose
Azure Blob Storage	Hosting and storing raw Olympic CSVs
Azure Databricks	Processing data using PySpark Notebooks
Python & SQL	Data transformation & querying
Power BI	Dashboard creation and data visualization

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🧱 Architecture: Bronze – Silver – Gold

Azure Blob Storage (.csv) ↓ Bronze Layer (Raw Mount) ↓ Silver Layer (Cleaned/Transformed) ↓ Gold Layer (Aggregated Tables) ↓ Power BI Dashboard

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📜 Description of Processing Layers

🔸 Bronze Layer

• Mounts raw Olympic .csv data from Azure Blob Storage to Databricks.
• Minimal processing; serves as the raw data zone.

🔸 Silver Layer

• Cleans and transforms raw data:
  • Handles nulls
  • Joins tables (athlete, sport, event, medal)
  • Formats columns

🔸 Gold Layer

• Generates aggregated insights and analysis-ready tables:
  • Medal counts by athlete, gender, year, and country
  • Total athletes by sport and season
  • Demographic metrics like GDP, population

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📊 Power BI Dashboards

🔵 Page 1 – Summary Overview

• 🥇 Total Medals: 27,497
• 👥 Total Athletes: 19,702
• 📊 Top Sports by Athlete Count
• 🌍 Medals by Country (Treemap)
• 🗺️ GDP by Country (Map)
• 🏅 Medal Counts by Athlete

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🟢 Page 2 – Gender & Demographics

• 👩‍🦱 Gender Distribution of Medals
• 📈 Population by Country
• 📍 Olympic Host Cities (Map)
• 🔎 Filters by Year, Gender, Country, Season

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🧠 Key Learnings

• Real-world Lakehouse architecture for data warehousing
• Data ingestion and transformation using Databricks + PySpark
• Seamless integration of Azure data services with Power BI
• Effective data storytelling through dashboards

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📎 Files You Can Explore

File/Folder	Purpose
O_Bronze.py	Bronze Layer logic (mount raw data)
O_Silver.py	Cleaned and transformed dataset logic
O_Gold.py	Aggregated Gold Layer metrics
olympicpbi.pdf	View dashboard if you don’t have Power BI
olympicp dashboard.pbix	Open and interact with the dashboard
images/	Screenshots for preview in README

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🔗 GitHub Repository

View This Project on GitHub

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👤 Author

Kushmi Anuththara
Master's in Data Science | Data Engineer | Azure & Power BI Enthusiast
📍 Based in Sweden
📫 LinkedIn

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📄 License

This project is licensed under the MIT License.