This project involves creating and managing data models using PostgreSQL. The project includes setting up dimension and fact tables, loading data from CSV files, and performing various data analysis tasks.
Data-Models/
├── Star_schema/
│ ├── data/
│ │ ├── sales_data.csv
│ │ ├── customer_data.csv
│ │ ├── product_data.csv
│ │ ├── location_data.csv
│ │ └── date_data.csv
│ ├── Customer Demographics Analysis.sql
│ ├── Sales by Year and Quarter.sql
│ ├── Total Sales by Location.sql
│ ├── Total Sales by Product.sql
│ ├── graph_visualiser-1725897085692.png
│ └── star.py
└── README.md
### Step-by-Step Guide
1. **Clone the repository:**
```bash
git clone <repository-url>
cd Data-Models
Setup Instructions
Prerequisites
PostgreSQL
Python
Pandas library
Step-by-Step Guide
Clone the repository:
git clone <repository-url>
cd Data-Models
Set up PostgreSQL database:
CREATE DATABASE data_models;
Create tables:
\c data_models
\i Star_schema/Customer\ Demographics\ Analysis.sql
\i Star_schema/Sales\ by\ Year\ and\ Quarter.sql
\i Star_schema/Total\ Sales\ by\ Location.sql
\i Star_schema/Total\ Sales\ by\ Product.sql
Load data from CSV files:
import pandas as pd
import psycopg2
conn = psycopg2.connect("dbname=data_models user=yourusername password=yourpassword")
cursor = conn.cursor()
sales_data = pd.read_csv('Star_schema/data/sales_data.csv')
customer_data = pd.read_csv('Star_schema/data/customer_data.csv')
product_data = pd.read_csv('Star_schema/data/product_data.csv')
location_data = pd.read_csv('Star_schema/data/location_data.csv')
date_data = pd.read_csv('Star_schema/data/date_data.csv')
# Insert data into tables (refer to the provided code snippets)
Run analysis scripts:
\i Star_schema/Customer\ Demographics\ Analysis.sql
\i Star_schema/Sales\ by\ Year\ and\ Quarter.sql
\i Star_schema/Total\ Sales\ by\ Location.sql
\i Star_schema/Total\ Sales\ by\ Product.sql
Usage
The star.py script contains the logic for loading data into the tables.
The SQL scripts in the Star_schema directory perform various data analysis tasks.
Contributing
Feel free to open issues or submit pull requests if you have any suggestions or improvements.
License
This project is licensed under the MIT License.
Contact
For any questions or inquiries, please contact follow git.
- The **Project Structure** section describes how files are organized.
- The **Setup Instructions** give a step-by-step guide to set up the project, including creating tables, loading data from CSV files, and running SQL scripts for analysis.
- The **Usage** section outlines what the main Python and SQL scripts do.
- The **Contributing** and **License** sections provide guidelines for contributing and state the project's