SupremEats2011 Data Warehouse Project

Introduction: \ This project, crafted by Grupo 1 for the Data Warehouse Architecture course under Professor Gustavo Tadeu Cicotoste, showcases a comprehensive design and implementation of a Data Warehouse solution. The project aims to illustrate the application of modern data warehousing techniques, including ETL processes, Online Analytical Processing (OLAP), and the integration with business intelligence tools.

Project Structure

supremeats-finalproject/
│
├── ML/                      # Python Script for making future sales prediction              
│
├── data/                    # Data files including raw and processed CSVs
│   ├── DW/
│   ├── factTables/
│   └── raw/
│
├── doc/                     # Documentation and assets
│   └── assets/
│   └── dashboard/
│   └── diagram/
│
├── scripts/                 # SQL and Python scripts for data processing
│
├── sql/                     # SQL scripts for DW database schema creation
│

Setup and Installation

Requirements

• Python 3.8+
• MySQL 8.0+
• Dash by Plotly
• Plotly

Installation Steps

1. Clone the repository:

   git clone https://github.com/marcelobarbugli/supremeatsDW 
   cd supremeats-finalproject

2. Install required Python libraries

   pip install dash plotly pandas

3. Database Setup

   • Execute the SQL scripts found in the sql/ directory to set up the database schema in MySQL.
   • Use the data import scripts to populate the database.

4. Running the Dashboard

   • Navigate to the dashboard/ directory.

     python dashboard.py

Data Architecture

This project utilizes a robust data warehousing model incorporating:

• Operational Data Store (ODS) for staging data.
• Data Marts for department-specific analysis.
• OLAP cubes for multidimensional data analysis.

Data Flow

• Data is extracted from various sources.
• The raw data undergoes transformation and cleaning.
• Processed data is loaded into the respective data marts and the central data warehouse.

Features

• Entity-Relationship Diagrams: Visual representations of data relationships.
• SQL Queries: For data manipulation and retrieval.
• Data Dashboards: Interactive dashboards for real-time data analysis.

Documentation

• Detailed documentation is available in the doc/ directory, which includes project reports, setup

Contributors

• Gisele Siqueira
• Marcelo Dozzi Babugli
• Diego Moura
• Matheus Higa
• Ricardo Geroto
• Roberto Eyama

Acknowledgments

Special thanks to Professor Gustavo Tadeu Cicotoste and Escola Politecnica da Universidade de São Paulo for their guidance and expertise throughout the development of this project.