Supermarket Sales Data Analytics | Modern Data Engineering GCP Project

Introduction

The goal of this project is to perform data analytics on Supermarket sales data using various tools and technologies, including GCP Storage, Python, Compute Instance, Mage Data Pipeline Tool, BigQuery, and Looker Studio.

Architecture

Technology Used

• Programming Language - Python

Google Cloud Platform

1. Google Storage
2. Compute Instance
3. BigQuery
4. Looker Studio

Modern Data Pipeine Tool - https://www.mage.ai/

Contibute to this open source project - https://github.com/mage-ai/mage-ai

Dataset Used

Supermarket Sales Data - This dataset contains sales of the 3 branches of supermarket. It has fields like Invoice id, date, time, customer type, gender, payment type, Unit price, Total, COGS, Tax percent, and few more.

Here is the dataset used in this project - https://github.com/vinayjain18/supermarket-sales-dataengineering-project/blob/main/data/supermarket_sales.csv

More info about dataset can be found here:
Kaggle Website - https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales

Data Model

Looker Studio

Dashboard link - https://lookerstudio.google.com/reporting/31aad5f0-6dd1-4836-aee0-e492e3aff3ea

YouTube Video

Video Link - https://youtu.be/FSeBuB8cbaU