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Saurabhkhandebharad / BigData-SK

Analyzed a multicategory e-commerce store using big data techniques on a Kaggle dataset with the help of AWS EC2, AWS S3, PySpark, AWS Glue ETL, AWS Athena, AWS CloudFormation, AWS Lambda and Power BI!

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awsaws-athenaaws-cloudformationaws-ec2aws-glue-crawleraws-lambdaaws-s3-bucketaws-servicesbig-dataend-to-end-pipelineend-to-end-projectkaggle-datasetpower-bipyspark

BigData - Saurabh Khandebharad

E - Commerce Analytics and Big Data Processing (End-To-End Group Project)

Guidance by - Pradeep Tripathi

KAGGLE DATASET: https://www.kaggle.com/datasets/mkechinov/ecommerce-behavior-data-from-multi-category-store

File Name: 2019-Nov.csv

File Size: 8 GB

Project Architecture

Architecture

Being excellent at data analysis and visualization, I volunteered to do the data cleaning and preprocessing in pyspark. Head over to PySpark.py and check my code! Handling such a large data was fun and a learning experience!

👉My PySpark Script

PowerBI Visualizations..

Page 1 - Dashboard Page1

Page 2 - Dashboard Page2


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About

Analyzed a multicategory e-commerce store using big data techniques on a Kaggle dataset with the help of AWS EC2, AWS S3, PySpark, AWS Glue ETL, AWS Athena, AWS CloudFormation, AWS Lambda and Power BI!

awsaws-athenaaws-cloudformationaws-ec2aws-glue-crawleraws-lambdaaws-s3-bucketaws-servicesbig-dataend-to-end-pipelineend-to-end-projectkaggle-datasetpower-bipyspark

Languages

Language:Python 100.0%