RetailPulse – End-to-End Retail Analytics Pipeline

📌 Overview

RetailPulse is an end-to-end data analytics project that simulates a real-world retail business pipeline.

It covers the full lifecycle:

• Data generation using Python
• Data storage in PostgreSQL
• Business analysis using SQL
• Visualization using Power BI

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🏗️ Architecture

Python (Data Generation)
        ↓
CSV Files (Staging)
        ↓
PostgreSQL (Data Warehouse)
        ↓
SQL (Business Analysis)
        ↓
Power BI (Dashboard & Insights)

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🛠️ Tech Stack

• Python (Pandas, NumPy, Faker)
• PostgreSQL
• SQL
• Power BI
• Git & GitHub

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🚀 Features

• Generated synthetic retail dataset (customers, products, transactions)
• Built ETL pipeline to load data into PostgreSQL
• Designed relational schema
• Performed SQL-based business analysis
• Built interactive Power BI dashboard

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📊 Dashboard

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📈 Key Insights

• Revenue trends fluctuate across months
• Electronics category contributes the highest revenue (~51%)
• Certain cities dominate overall sales performance
• Customer spending is highly concentrated among top users (high CLTV)

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📂 Project Structure

RetailPulse/
 ├── python/
 │    ├── generate_data.py
 │    ├── load_to_db.py
 ├── sql/
 │    ├── business_analysis.sql
 ├── images/
 │    ├── dashboard_overview.png
 ├── README.md
 ├── .gitignore

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▶️ How to Run

1. Generate data:

python python/generate_data.py

2. Load data into PostgreSQL:

python python/load_to_db.py

3. Run SQL queries:

• Open sql/business_analysis.sql in PostgreSQL

4. Open Power BI dashboard:

• Load .pbix file (if included)

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🎯 Outcome

This project demonstrates the ability to build a complete data pipeline and derive actionable business insights.