https://github.com/nada-ibrahim12/coffeeshop-project
Coffee Shop Data Warehouse | SQL Server + SSIS + Power BI ETL pipelines, star schema, and dashboards for sales, staff, recipes & customer analytics. Retail BI demo project.
https://github.com/nada-ibrahim12/coffeeshop-project
database datawarehouse etl pipeline powerbi snowflake sql sql-server ssis starschema
Last synced: 4 months ago
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Coffee Shop Data Warehouse | SQL Server + SSIS + Power BI ETL pipelines, star schema, and dashboards for sales, staff, recipes & customer analytics. Retail BI demo project.
- Host: GitHub
- URL: https://github.com/nada-ibrahim12/coffeeshop-project
- Owner: Nada-ibrahim12
- Created: 2025-05-02T19:55:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-12T19:46:10.000Z (11 months ago)
- Last Synced: 2025-08-15T11:18:42.033Z (10 months ago)
- Topics: database, datawarehouse, etl, pipeline, powerbi, snowflake, sql, sql-server, ssis, starschema
- Homepage:
- Size: 32.3 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CoffeeShop Data Warehouse Project
## Overview
This project delivers a complete data warehouse solution for a fictional coffee shop chain. It supports robust business analytics and operational insights by integrating transactional, periodic, and dimensional data.
Key objectives:
- Track sales transactions, staff costs, recipe profitability, and customer behavior.
- Enable performance monitoring through KPIs such as Total Sales Revenue, Staff Efficiency, Recipe Profitability, and Customer Visit Frequency.
The project uses **SQL Server** for data storage, **SSIS** for ETL workflows, and **Power BI** for interactive dashboards.
---
## Source System
[Used Dataset](https://www.kaggle.com/datasets/viramatv/coffee-shop-data)
The source operational data comes from normalized tables covering:
- Orders, Items, Inventory
- Ingredients and Recipes
- Staff, Rotas, Shifts
- Customers
**ER Diagram:**
---
## Business Processes Modeled
1. Sales Tracking
2. Staff Cost Management
3. Recipe Cost Analysis
4. Monthly Customer Visit Analysis
---
## Fact Tables
| Fact Table | Type | Grain | Measures |
|-------------------------------|------------------|----------------------------------|-----------------------------------|
| Fact_Sales | Transactional | Per item per order | `item_quantity`, `sales_amount` |
| Fact_Staff_Cost | Snapshot | Per staff per shift per day | `worked_hours`, `cost` |
| Fact_Recipe | Transactional | Per recipe-item | `item_price`, `total_cost`, `profit` |
| Fact_Customer_Visits_Monthly | Snapshot | Per customer per month per year | `visit_count`, `total_spent` |
---
## Dimension Tables
| Dimension | Type | Notes |
|----------------|-------------------------|-----------------------------------------------|
| Dim_Staff | SCD Type 2 | Tracks historical role and salary changes |
| Dim_Item | SCD Type 1, Conformed | Shared across Fact_Sales and Fact_Recipe |
| Dim_Recipe | SCD Type 1 | Stores recipe details and metadata |
| Dim_Customer | SCD Type 2, Conformed | Captures customer changes for trend analysis |
| Dim_Shift | Static | Defines staff time blocks |
| Dim_Ingredient | Static | Supports cost breakdowns |
| Dim_Date | Role Playing | Supports time-series analytics |
**Star Schema:**
---
## ETL Pipeline
**ETL Architecture:**
- **Staging Area:** Raw data landing and preparation.
- **Data Flow Tasks:** Includes lookups, derived columns, and conditional routing.
- **Control Flow:** Automates truncate/load steps, config updates, and error handling.
**Key Packages:**
- `Fact_Sales.dtsx` — Daily, 2:00 AM
- `Fact_Staff_Cost.dtsx` — Daily, 2:00 AM
- `Fact_Recipe.dtsx` — Daily, 2:00 AM
- `Fact_Customer_Visits_Monthly.dtsx` — Monthly, 2:00 AM
Packages run on schedule via SQL Server Agent Jobs.
---
## Analytical Insights
Example business metrics include:
- Total Sales Revenue
- Monthly Staff Cost
- Most Profitable Recipes
- Top 5 Customers by Spending
- Staff Cost per Hour and Sales per Hour
- Customer Loyalty Score
More than 30 analytical SQL queries were written and validated to ensure accuracy and performance.
---
## Power BI Reporting
Interactive dashboards include:
- **Sales Overview:** Sales by item, order type, daily trends.
- **Staff Dashboard:** Cost per shift, productivity analysis.
- **Recipe Analysis:** Profitability by item and ingredient cost breakdown.
- **Customer Insights:** Visit frequency, spending habits, loyalty trends.
Dashboards feature dynamic filtering, drill-downs, and visual KPIs to support decision-making.
---
## Project Summary
This project demonstrates:
- Solid dimensional modeling using a star schema.
- Automated ETL design with SSIS.
- Reliable data mart creation.
- Actionable, interactive dashboards in Power BI.
It provides a scalable solution for retail analytics in the coffee shop industry.
---
## Full Documentation
All project details, diagrams, and analysis are documented here:
[Project Report PDF](https://github.com/Nada-ibrahim12/CoffeeShop-Project/blob/main/DWH_Project_20220358_20220015_20220345.pdf)