https://github.com/firaskahlaoui/retail-data-warehouse

This project demonstrates the creation of a Data Warehouse using SQL Server 2022. It includes the design of dimension and fact tables, ETL processes for data integration, Python scripts for synthetic data generation, and SQL queries for KPI analysis to support business decision-making.
https://github.com/firaskahlaoui/retail-data-warehouse

analytics database databasedesign datawarehousing etl kpi python sqlserver-2022

Last synced: 11 months ago
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This project demonstrates the creation of a Data Warehouse using SQL Server 2022. It includes the design of dimension and fact tables, ETL processes for data integration, Python scripts for synthetic data generation, and SQL queries for KPI analysis to support business decision-making.

• Host: GitHub
• URL: https://github.com/firaskahlaoui/retail-data-warehouse
• Owner: FirasKahlaoui
• Created: 2024-12-23T15:34:40.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-12-25T00:41:49.000Z (over 1 year ago)
• Last Synced: 2024-12-25T01:21:54.641Z (over 1 year ago)
• Topics: analytics, database, databasedesign, datawarehousing, etl, kpi, python, sqlserver-2022
• Language: Python
• Homepage:
• Size: 865 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Data Warehouse Project

![DataWarehouse Star Schema](https://github.com/FirasKahlaoui/retail-data-warehouse/blob/master/DataWarehouse%20Schema/DataWarehouse%20StarSchema%20White.png)

## Data Warehouse Project with SQL Server

This project demonstrates the construction of a fully functional Data Warehouse using SQL Server 2022 and Visual Studio 2022. It includes:

- **Database Design**: Creation of dimension tables and a fact table for a retail sales scenario.

- **ETL Processes**: Implementation of ETL pipelines to load and transform data from staging tables into the final schema.

- **Data Generation**: Python scripts to generate synthetic datasets for testing.

- **KPI Analysis**: SQL queries to calculate key metrics, such as total sales, top-selling products, and revenue by region.

The repository includes SQL scripts, Python scripts for data generation, and documentation on setting up and running the project.

## Project Structure

- **.gitignore**: Configuration file to specify files and directories to be ignored by Git.

- **DataWarehouse/**: Main directory containing the SQL Server project files.

    - **bin/Debug/**: Build output directory for debug configuration.

    - **DataWarehouse.dbmdl**: Database model file.

    - **DataWarehouse.jfm**: JSON file for project metadata.

    - **DataWarehouse.sln**: Visual Studio solution file.

    - **DataWarehouse.sqlproj**: SQL Server project file.

    - **DataWarehouse.sqlproj.user**: User-specific project settings.

- **Python Scripts/**: Directory containing Python scripts for data generation.

- **Results/**: Directory containing the KPIs results.

- **SQL Scripts/**: Directory containing SQL scripts.

- **Data/**: Directory containing Tunisian-specific data.

- **DataWarehouse Schema/**: Directory containing schema diagrams.

- **README.md**: Project documentation file.