🧹 Cleaning & Analyzing Customer Data on AWS

📘 About the Project

This project demonstrates how to build a serverless data cleaning and transformation pipeline using Amazon S3, Amazon Athena, and AWS Glue DataBrew. It focuses on uploading raw customer data to S3, cleaning and validating it using SQL queries in Athena, and visually preparing data through Glue DataBrew recipes — resulting in a structured, analysis-ready dataset.

The workflow combines both SQL-based and no-code data cleaning approaches, showcasing how AWS services can simplify ETL (Extract, Transform, Load) processes for real-world datasets.