Amazon Sales Rapport Data Warehouse Project

Overview

The Amazon Sales Rapport Data Warehouse (DW) project aims to create a comprehensive data infrastructure for analyzing and reporting on sales data from Amazon. It involves building a robust data warehouse architecture, implementing Extract, Transform, Load (ETL) processes, and enabling visualization and forecasting capabilities.

Project Components

1. ETL Process

   • Extraction: Data is extracted from various sources, including CSV and Excel files, using Python scripts.
   • Transformation: Extracted data undergoes transformation steps to ensure consistency, handle missing values, and prepare it for loading into the data warehouse.
   • Loading: Transformed data is loaded into the data warehouse tables, including the sales fact table and dimension tables for customers, products, dates, and geography.

2. Data Warehouse Architecture

   • The data warehouse architecture includes tables for storing sales data (fact table) and related dimensions such as date, geography, product, and customer.
   • Indexes are created to optimize query performance, ensuring efficient retrieval of data.

3. Update Process

   • The project implements a robust update process to maintain data integrity and consistency.
   • Existing data in the data warehouse is compared with new data to identify duplicates and update the warehouse accordingly.
   • Here is a helpful tool GUI for the ETL :

4. Visualization and Reporting

   • After updating the data warehouse, visualization and reporting capabilities are enabled using Power BI.
   • Power BI is used to create reports and dashboards for analyzing sales trends and performance.

5. Sales Forecasting

   • The project integrates with R for sales forecasting.
   • Time series analysis and forecasting techniques are applied to predict future sales trends based on historical data.

How to Use

1. Clone the Repository: Clone this GitHub repository to your local machine.
2. Install Dependencies: Ensure you have Python, MySQL, and necessary Python packages installed. Additionally, Power BI Desktop is required.
3. Install Required Packages: Use pip install -r requirements.txt to install the necessary Python packages listed in the requirements.txt file.
4. Run ETL Process: Execute the Python scripts to perform the ETL process and update the data warehouse.
5. Explore Data: Utilize Power BI to explore and visualize the sales data stored in the data warehouse.
6. Forecast Sales: Utilize R for sales forecasting based on historical data.

This project is made by Farouk Daboussi .

Contributions to the project are welcome! Feel free to submit bug reports, feature requests, or pull requests.

License

This project is licensed under the MIT License.