https://github.com/yash22222/olympic-games-analytics-using-apache-spark
The "Olympic Games Analytics Using Apache Spark Databricks" project explores data from the Olympic Games (1896-2016) to identify trends and insights. Using Apache Spark for big data processing and Databricks for visualization, the project analyzes key factors like top-performing countries and athlete attributes, showcasing real-world analytics.
https://github.com/yash22222/olympic-games-analytics-using-apache-spark
apache apache-kafka apache-spark big-data-analytics csv data data-analytics data-visualization databricks excel mysql olympics regions
Last synced: 7 months ago
JSON representation
The "Olympic Games Analytics Using Apache Spark Databricks" project explores data from the Olympic Games (1896-2016) to identify trends and insights. Using Apache Spark for big data processing and Databricks for visualization, the project analyzes key factors like top-performing countries and athlete attributes, showcasing real-world analytics.
- Host: GitHub
- URL: https://github.com/yash22222/olympic-games-analytics-using-apache-spark
- Owner: Yash22222
- License: mit
- Created: 2024-10-16T05:53:01.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-16T07:18:59.000Z (12 months ago)
- Last Synced: 2025-03-29T02:43:33.074Z (7 months ago)
- Topics: apache, apache-kafka, apache-spark, big-data-analytics, csv, data, data-analytics, data-visualization, databricks, excel, mysql, olympics, regions
- Language: HTML
- Homepage: https://yashashokshirsath.netlify.app/
- Size: 18.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Olympic Games Analytics Using Apache Spark
## Table of Contents
1. [Project Overview](#project-overview)
2. [Motivation](#motivation)
3. [Objectives](#objectives)
4. [Technologies Used](#technologies-used)
5. [Architecture Overview](#architecture-overview)
6. [Data Sources](#data-sources)
7. [Installation and Setup](#installation-and-setup)
8. [Usage](#usage)
9. [Features](#features)
10. [Results](#results)
11. [Conclusion](#conclusion)
12. [Future Work](#future-work)
13. [Acknowledgments](#acknowledgments)
14. [References](#references)
## Project Overview
The **Olympic Games Analytics** project aims to analyze data from the Olympic Games using Apache Spark and Databricks. By leveraging Big Data technologies, this project explores historical trends, medal distributions, and athlete demographics, providing valuable insights into the performances and achievements in the Olympic Games from Athens 1896 to Rio 2016.
## Motivation
With the increasing volume of sports data, traditional analytical methods often fall short in handling large datasets efficiently. This project seeks to utilize Apache Spark’s capabilities to process and analyze Olympic data, highlighting trends and patterns that can inform future sporting strategies and decisions.
## Objectives
- To load and preprocess Olympic Games data using Apache Spark.
- To explore trends in medal distribution and athlete demographics.
- To visualize findings using graphical representations.
- To provide insights into the performance of countries over time.
## Technologies Used
- **Apache Spark**: For distributed data processing.
- **Databricks**: For creating and running Spark notebooks.
- **Python**: For data manipulation and analysis.
- **SparkSQL**: For querying structured data.
- **Matplotlib/Seaborn**: For data visualization.
## Architecture Overview
The architecture of the project consists of the following components:
1. **Data Ingestion**: Load data into Spark DataFrames from CSV files.
2. **Data Processing**: Clean and preprocess the data for analysis.
3. **Data Analysis**: Use SparkSQL and DataFrames for querying and insights.
4. **Data Visualization**: Create visualizations to represent findings.
5. **Reporting**: Summarize the analysis in a structured report.
## Data Sources
The dataset used in this project includes historical Olympic Games data from various sources, encompassing all the Games from Athens 1896 to Rio 2016. The dataset includes information about medals, athletes, events, and countries.
## Installation and Setup
To run this project, you need:
1. A Databricks account. Sign up for a [free Community Edition](https://databricks.com/try-databricks).
2. The Olympic dataset in CSV format.
### Steps to Set Up
1. Create a new Databricks notebook in your Databricks workspace.
2. Upload the Olympic dataset to Databricks.
3. Load the dataset into a Spark DataFrame using PySpark.
```python
# Sample code to load data
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("OlympicGamesAnalytics").getOrCreate()
df = spark.read.csv("path/to/olympic_data.csv", header=True, inferSchema=True)
```
## Usage
Once the dataset is loaded and preprocessed, you can analyze it using various Spark operations and SQL queries.
### Sample Queries
```sql
-- Top 5 countries by gold medals
SELECT Country, COUNT(Medal) as Gold_Medals
FROM olympic_data
WHERE Medal = 'Gold'
GROUP BY Country
ORDER BY Gold_Medals DESC
LIMIT 5;
```
## Features
- **Data Ingestion**: Efficiently loads large datasets.
- **Data Analysis**: Supports complex queries using SparkSQL.
- **Visualization**: Provides graphical representations of data insights.
- **Reporting**: Summarizes findings in a structured format.
## Results
The project yields insights into various aspects of the Olympic Games, such as:
- Trends in medal distribution over the years.
- Performance metrics of athletes by country and gender.
- Analysis of age and weight of medalists over time.
## Conclusion
The Olympic Games Analytics project illustrates the effectiveness of Apache Spark in handling large-scale sports data analysis. The insights gained can aid stakeholders in understanding trends and enhancing decision-making in sports.
## Future Work
Future enhancements may include:
- Extending the analysis to more recent Olympic data (Tokyo 2020 and beyond).
- Integrating machine learning models for predictive analytics.
- Implementing a web application to display results interactively.
## View Our Project Here
[Olympic Games Analytics Using Apache Spark](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1157101155436598/3859535939939444/4730792539126384/latest.html)
## References
1. Databricks. (n.d.). *What is Databricks?* Retrieved from [Databricks Documentation](https://docs.databricks.com/)
2. Apache Spark. (n.d.). *Apache Spark Documentation.* Retrieved from [Apache Spark](https://spark.apache.org/docs/latest/)
3. Olympic.org. (n.d.). *Olympic Games Results.* Retrieved from [Olympic Games](https://olympics.com/en/olympic-games)
4. Zhang, Y., & Zhao, S. (2020). "Analysis of Olympic Games Data Using Big Data Technologies." *International Journal of Sports Analytics*, 6(2), 123-135.
5. Apache Software Foundation. (n.d.). *Spark SQL, DataFrames and Datasets Guide.* Retrieved from [Apache Spark SQL](https://spark.apache.org/sql/)