Food Data Warehouse and Data Analysis

The project is in master Branch

Description

A project focused on creating a data warehouse for Tunisian food data using Faker to generate sample data. The data is centralized into a database and organized into an OLAP cube for efficient analysis. The final step involves visualizing the data for insightful analysis and reporting

Features

• 🧩 Generate Data Warehouse with Faker: Use Python and Faker library to generate sample data for the food data warehouse.
• 🧩 Import Data to SQL Server Database: Import the generated data into an SQL Server database for storage and management.
• 🧩 Connect to SSAS and Create OLAP Cube: Connect to SQL Server Analysis Services (SSAS) and create an OLAP cube for data analysis.
• 🧩 Create Customized Date Dimension: Design and implement a customized date dimension and relate it to the fact table.
• 🧩 Process Cube: Process the OLAP cube to ensure data is up-to-date and ready for analysis.
• 💾 Test with Excel: Test the OLAP cube and data integration using Excel for initial validation.
• 🔍 Visualize with Customized Tool: Use my customized development tool to create smooth and insightful reports for data visualization.

Getting Started

Prerequisites

• Visual Studio 2019
• SSDT
• Sql Server 2019
• SSAS
• Analysis Service
• Microsoft Excel
• Faker
• Python

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Mohamed Nour KHammeri - @My-Web-Site - mednour.khl@gmail.com

Project Link: https://github.com/mednour2019/fake-DHW-Food