https://github.com/maazie-khan/olympics-data-enigeering

Worked with Azure Data Factory, Databricks, Data Lake Storage, and Synapse Analytics to build an ETL pipeline for processing and analyzing Olympic Games data from Kaggle.
https://github.com/maazie-khan/olympics-data-enigeering

azure big-data data-analysis dataengineering devops pipeline

Last synced: 3 days ago
JSON representation

Worked with Azure Data Factory, Databricks, Data Lake Storage, and Synapse Analytics to build an ETL pipeline for processing and analyzing Olympic Games data from Kaggle.

• Host: GitHub
• URL: https://github.com/maazie-khan/olympics-data-enigeering
• Owner: Maazie-Khan
• Created: 2024-09-12T21:33:32.000Z (21 days ago)
• Default Branch: main
• Last Pushed: 2024-09-12T21:40:10.000Z (21 days ago)
• Last Synced: 2024-09-30T16:14:27.944Z (3 days ago)
• Topics: azure, big-data, data-analysis, dataengineering, devops, pipeline
• Language: Jupyter Notebook
• Homepage:
• Size: 3.51 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Olympic Games Data Engineering Pipeline (using Azure)

This repository contains the implementation of an ETL pipeline using **Azure Cloud** services to process **Olympic Games data** from Kaggle.

## Overview

The project extracts data from Kaggle, transforms it using **Apache Spark** in **Azure Databricks**, loads the cleaned data into **Azure Data Lake Storage**, and analyzes it using **Azure Synapse Analytics**.

![architecture](https://github.com/user-attachments/assets/459faf87-c6d7-48d1-aa56-ce1170db48d9)

### Objectives:

- **Extract** data from Kaggle.

- **Transform** using Spark.

- **Load** into Azure Data Lake Storage.

- **Analyze** using SQL and visualizations in Synapse.

## Solution

1. **Data Extraction**: 

   - Data is extracted from Kaggle via **Azure Data Factory** and stored in **Azure Data Lake Storage**.

2. **Data Transformation**: 

   - **Azure Databricks** and **Apache Spark** are used to clean and transform the data.

3. **Data Loading**: 

   - The transformed data is reloaded into **Azure Data Lake Storage**.

4. **Data Analysis**: 

   - **Azure Synapse Analytics** runs SQL queries and generates visualizations.

## Tech Stack

- **Azure Data Factory**: Data extraction and orchestration.

- **Azure Databricks**: Data transformation using Apache Spark.

- **Azure Data Lake Storage**: Scalable data storage.

- **Azure Synapse Analytics**: Data querying and visualization.

## Insights

The project allows analysis of Olympic data, such as:

- **Medal Distribution** by country and year.

- **Athlete Trends** in age and performance.

- **Event Performance** analysis across nations.

## Repository Structure

- `notebooks/`: Databricks notebooks for data transformation.

- `queries/`: SQL queries for analysis in Synapse.

## How to Run

1. Set up **Azure Data Factory**, **Databricks**, **Data Lake Storage**, and **Synapse Analytics**.

2. Extract data from Kaggle via **Data Factory**.

3. Transform the data in **Databricks**.

4. Load and query the data in **Synapse**.

## Preview

https://github.com/user-attachments/assets/08ca8988-ebe6-406b-9dfe-47adea61b670