{"id":30252111,"url":"https://github.com/nada-ibrahim12/coffeeshop-project","last_synced_at":"2026-02-11T15:31:03.695Z","repository":{"id":304397747,"uuid":"976814375","full_name":"Nada-ibrahim12/CoffeeShop-Project","owner":"Nada-ibrahim12","description":"Coffee Shop Data Warehouse | SQL Server + SSIS + Power BI   ETL pipelines, star schema, and dashboards for sales, staff, recipes \u0026 customer analytics. Retail BI demo project.  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It supports robust business analytics and operational insights by integrating transactional, periodic, and dimensional data.\n\nKey objectives:\n- Track sales transactions, staff costs, recipe profitability, and customer behavior.\n- Enable performance monitoring through KPIs such as Total Sales Revenue, Staff Efficiency, Recipe Profitability, and Customer Visit Frequency.\n\nThe project uses **SQL Server** for data storage, **SSIS** for ETL workflows, and **Power BI** for interactive dashboards.\n\n---\n\n## Source System\n[Used Dataset](https://www.kaggle.com/datasets/viramatv/coffee-shop-data)\n\n\nThe source operational data comes from normalized tables covering:\n\n- Orders, Items, Inventory  \n- Ingredients and Recipes  \n- Staff, Rotas, Shifts  \n- Customers\n\n**ER Diagram:**  \n\u003cimg width=\"1087\" height=\"748\" alt=\"image\" src=\"https://github.com/user-attachments/assets/b8d7fe7f-1b57-4de0-a337-116e9fb2d100\" /\u003e\n\n\n---\n\n## Business Processes Modeled\n\n1. Sales Tracking  \n2. Staff Cost Management  \n3. Recipe Cost Analysis  \n4. Monthly Customer Visit Analysis\n\n---\n\n## Fact Tables\n\n| Fact Table                    | Type             | Grain                            | Measures                          |\n|-------------------------------|------------------|----------------------------------|-----------------------------------|\n| Fact_Sales                    | Transactional    | Per item per order               | `item_quantity`, `sales_amount`   |\n| Fact_Staff_Cost               | Snapshot         | Per staff per shift per day      | `worked_hours`, `cost`            |\n| Fact_Recipe                   | Transactional    | Per recipe-item                  | `item_price`, `total_cost`, `profit` |\n| Fact_Customer_Visits_Monthly  | Snapshot         | Per customer per month per year  | `visit_count`, `total_spent`      |\n\n---\n\n## Dimension Tables\n\n| Dimension      | Type                    | Notes                                         |\n|----------------|-------------------------|-----------------------------------------------|\n| Dim_Staff      | SCD Type 2              | Tracks historical role and salary changes     |\n| Dim_Item       | SCD Type 1, Conformed   | Shared across Fact_Sales and Fact_Recipe      |\n| Dim_Recipe     | SCD Type 1              | Stores recipe details and metadata            |\n| Dim_Customer   | SCD Type 2, Conformed   | Captures customer changes for trend analysis  |\n| Dim_Shift      | Static                  | Defines staff time blocks                     |\n| Dim_Ingredient | Static                  | Supports cost breakdowns                      |\n| Dim_Date       | Role Playing            | Supports time-series analytics                |\n\n**Star Schema:**  \n\u003cimg width=\"1048\" height=\"741\" alt=\"image\" src=\"https://github.com/user-attachments/assets/cd1577ac-6677-480c-b50e-73b3711b24ce\" /\u003e\n\n---\n\n## ETL Pipeline\n\n**ETL Architecture:**\n- **Staging Area:** Raw data landing and preparation.\n- **Data Flow Tasks:** Includes lookups, derived columns, and conditional routing.\n- **Control Flow:** Automates truncate/load steps, config updates, and error handling.\n\n**Key Packages:**\n- `Fact_Sales.dtsx` — Daily, 2:00 AM\n- `Fact_Staff_Cost.dtsx` — Daily, 2:00 AM\n- `Fact_Recipe.dtsx` — Daily, 2:00 AM\n- `Fact_Customer_Visits_Monthly.dtsx` — Monthly, 2:00 AM\n\nPackages run on schedule via SQL Server Agent Jobs.\n\n---\n\n## Analytical Insights\n\nExample business metrics include:\n- Total Sales Revenue\n- Monthly Staff Cost\n- Most Profitable Recipes\n- Top 5 Customers by Spending\n- Staff Cost per Hour and Sales per Hour\n- Customer Loyalty Score\n\nMore than 30 analytical SQL queries were written and validated to ensure accuracy and performance.\n\n---\n\n## Power BI Reporting\n\nInteractive dashboards include:\n- **Sales Overview:** Sales by item, order type, daily trends.\n- **Staff Dashboard:** Cost per shift, productivity analysis.\n- **Recipe Analysis:** Profitability by item and ingredient cost breakdown.\n- **Customer Insights:** Visit frequency, spending habits, loyalty trends.\n\nDashboards feature dynamic filtering, drill-downs, and visual KPIs to support decision-making.\n\n---\n\n## Project Summary\n\nThis project demonstrates:\n- Solid dimensional modeling using a star schema.\n- Automated ETL design with SSIS.\n- Reliable data mart creation.\n- Actionable, interactive dashboards in Power BI.\n\nIt provides a scalable solution for retail analytics in the coffee shop industry.\n\n---\n\n## Full Documentation\n\nAll project details, diagrams, and analysis are documented here:  \n[Project Report PDF](https://github.com/Nada-ibrahim12/CoffeeShop-Project/blob/main/DWH_Project_20220358_20220015_20220345.pdf)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnada-ibrahim12%2Fcoffeeshop-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnada-ibrahim12%2Fcoffeeshop-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnada-ibrahim12%2Fcoffeeshop-project/lists"}