📊 Data Warehouse & NoSQL Project

🏆 Objective

The goal of this project is to design and implement a Data Warehouse (DW) and explore the use of NoSQL technologies to manage heterogeneous data. We use Neo4j and Cypher for graph-based data representation.

📌 Key Features

• Data integration and transformation from heterogeneous sources
• Data modeling in graph form in Neo4j
• Advanced querying with Cypher
• Visualization of relationships and data exploration
• Clustering analysis and Big Data management

🛠 Technologies Used

• Neo4j: Graph database
• Cypher: Query language for Neo4j
• Python: For data extraction and processing
• Pandas: For handling tabular data
• Graph Data Science (GDS): For advanced graph analysis

📂 Project Structure

📦 data-warehouse-project
 ┣ 📂 SQL                # Queries for the Data Warehouse
 ┣ 📂 df_for_Neo4j       # Relations and Nodes for Cypher
 ┣ 📜 README.md          # Documentation of the project
 ┣ 📜 requirements.txt   # Python requirements
 ┣ 📜 graph.cypher       # Queries for cypher
 ┣ 📜 ml2.ipynb          # Machine learning for k-means algo
 ┣ 📜 prep_bdd.ipynb     
 ┣ 📜 schema.md          # Star Schema for data integration
 ┣ 📜 viz2.ipynb         # Data visualization

📥 Installation

1️⃣ Prerequisites

• Neo4j Desktop
• Python 3.8+

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Running Neo4j

Using Neo4j Desktop:

• Create a local database
• Set the credentials (neo4j / password)
• Enable the Graph Data Science (GDS) plugin

🚀 Usage

Loading Data into Neo4j

Run the data loading script:

python scripts/load_data.py

Running Cypher Queries

Access Neo4j Browser and run queries like:

MATCH (n) RETURN n LIMIT 10;

📈 Data Analysis and Visualization

The Jupyter notebooks contain clustering analysis and graph visualizations. To run them:

jupyter notebook

📌 Authors

• Nom 1 (@GuillaumeDeSaintEtienne)
• Nom 2 (@MaelGalliou)
• Nom 3 (@Sachafrft)
• Nom 4 (@emiliengodet)

📜 Licence

This project is licensed under the MIT License. You are free to use and modify it as needed.

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🚀 Happy project and enjoy data exploration !