A Modern Business Intelligence Dashboard for Sales & Operations Planning.
SalesPulse is a full-stack analytics application designed to simulate real-world business intelligence workflows. It ingests raw transactional data into a normalized PostgreSQL database, processes it using Python (SQLAlchemy/Pandas), and visualizes key performance indicators (KPIs) via an interactive Streamlit dashboard.
This project follows industry-standard data engineering practices:
- Database: PostgreSQL (Relational Data Warehousing)
- Backend Logic: Python, SQLAlchemy, Pandas (ETL & Analysis)
- Frontend: Streamlit (Interactive Dashboard)
- Visualization: Plotly (Dynamic Charts)
- Infrastructure: Docker-ready, Environment Variable Configuration
- Automated Data Seeding: Instantly generates thousands of mock transactions (Orders, Customers, Products, Regions) for testing.
- Advanced SQL Analytics: Uses Complex SQL (Joins, Aggregations, Window Functions) to calculate Month-over-Month growth and retention.
- Interactive Filtering: Real-time data slicing by Region (North, South, East, West).
- Business KPIs: Tracks Total Revenue, Active Customers, AOV (Average Order Value), and Top Selling Products.
- Python 3.10+
- PostgreSQL installed locally (or via Docker)
git clone [https://github.com/yourusername/salespulse-analytics.git](https://github.com/yourusername/salespulse-analytics.git)
cd salespulse-analytics