# π Data Warehousing and Advanced Data Analytics π Welcome to the **Data Warehousing and Advanced Data Analytics** project! This repository showcases a comprehensive data analytics project that analyzed promotions and provided tangible insights to the Sales Director. ## π Table of Contents 1. [Project Overview](#project-overview) 2. [Features](#features) 3. [Technologies Used](#technologies-used) 4. [Data Flow](#data-flow) 5. [Data Analysis Techniques](#data-analysis-techniques) 6. [Data Visualization](#data-visualization) 7. [Getting Started](#getting-started) 8. [Installation](#installation) 9. [Usage](#usage) 10. [Contributing](#contributing) 11. [License](#license) 12. [Releases](#releases) ## π Project Overview This project aims to provide a detailed analysis of promotional data and its impact on sales. By employing advanced data analytics techniques, we extract valuable insights that guide business decisions. The data architecture is designed to support efficient data flow and modeling, enabling quick access to the information required by the Sales Director. ## π Features - In-depth analysis of promotional campaigns - Segmentation of customer data for targeted marketing - Interactive visualizations to represent data insights - Data warehousing solutions for efficient storage and retrieval - End-to-end ETL pipeline for data processing ## π» Technologies Used This project utilizes a variety of technologies, including: - **Data Warehousing:** MSSQL for data storage - **ETL:** Efficient extraction, transformation, and loading of data - **Data Visualization:** Tableau for creating interactive dashboards - **Containerization:** Docker for consistent development environments ## π Data Flow The data flow within this project follows a systematic approach. It starts with data collection, followed by transformation and analysis, leading to visualization. Below is a simplified overview of the data flow: 1. **Data Collection:** Gather data from various sources 2. **Data Cleaning:** Remove inconsistencies and prepare for analysis 3. **Data Transformation:** Convert data into a usable format 4. **Data Analysis:** Perform analytical tasks to derive insights 5. **Data Visualization:** Present insights through visual formats ## π Data Analysis Techniques In this project, we applied several data analysis techniques: - **Descriptive Analysis:** Summarize historical data to understand trends - **Predictive Analysis:** Use statistical models to predict future outcomes - **Prescriptive Analysis:** Recommend actions based on analysis ## π Data Visualization Data visualization plays a critical role in conveying insights effectively. We used Tableau to create interactive dashboards, allowing users to explore data intuitively. Here are some key visualizations included in the project: - Sales trends over time - Customer segmentation based on purchasing behavior - Promotional campaign performance metrics ## π οΈ Getting Started To get started with this project, follow the instructions below. ### Installation 1. Clone this repository: ```bash git clone https://github.com/EngIbrahim1/Data-Warehousing-and-Advanced-Data-Analytics.git
- Navigate to the project directory:
cd Data-Warehousing-and-Advanced-Data-Analytics - Set up the required environment using Docker:
docker-compose up
To analyze the data:
- Ensure the data files are in the appropriate format.
- Run the ETL pipeline to load data into the database:
python etl_pipeline.py
- Open Tableau and connect to the database for visualization.
Contributions are welcome! If you have suggestions for improvements or would like to report an issue, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
You can find the latest releases of this project here. Please download the relevant files and execute them as needed.
This repository covers the following topics:
- data
- data analysis
- data architecture
- data flow analysis
- data modeling
- data pipeline
- data segmentation
- data visualization
- data warehousing
- docker
- etl
- etl pipeline
- mssql
- sql
- tableau
Feel free to explore the project, and letβs drive data-driven decisions together! π‘