IPL Data Analysis Project

Overview

This is an end-to-end data engineering project focused on analyzing IPL (Indian Premier League) data. The project demonstrates data ingestion, processing, and analysis using various tools and technologies in the AWS ecosystem and Apache Spark.

Tech Stack

• AWS S3: Used for storing raw and processed data.
• Databricks Community Edition: Utilized for Spark programming and notebook-based data processing.
• PySpark: Employed for data transformation and analysis using Spark's powerful API.
• SQL: Used for querying and performing effective data analysis.

Project Structure

1. Data Ingestion: Loading raw IPL data into AWS S3.
2. Data Processing:
   • Using Databricks notebooks to process and transform data.
   • Implementing PySpark to handle large-scale data transformations.
3. Data Analysis:
   • Performing analysis and generating insights from IPL data.
   • Visualizing results using appropriate tools.

Setup Instructions

Prerequisites

• AWS Account with access to S3
• Databricks Community Edition account
• Basic knowledge of PySpark and SQL

Steps

1. AWS S3 Setup:

   • Create an S3 bucket to store IPL data files.

2. Databricks Community Edition:

   • Create a new notebook for PySpark programming.
   • Connect to your S3 bucket to access the data.

3. PySpark and SQL:

   • Implement data transformation and analysis using PySpark.
   • Write SQL queries to derive insights from the data.

How to Run

1. Upload Data to S3: Place your IPL dataset files in the designated S3 bucket.
2. Execute Databricks Notebooks: Run the Databricks notebooks to process and analyze the data.
3. Review Results: Check the results and visualizations generated from the analysis.

Contributing

Feel free to contribute to this project by submitting issues or pull requests. Your suggestions and improvements are welcome!

Contact

For any questions or feedback, please reach out to [email protected].