Python-SQL-Project

ETL - Project

📊 ETL Pipeline: Kaggle → Python → MS SQL Server
This project demonstrates a complete ETL (Extract, Transform, Load) workflow:

• Extracted data from Kaggle using the Kaggle API key
• Transformed and cleaned the dataset using Python (Pandas)
• Loaded the refined data into Microsoft SQL Server
• Performed structured SQL queries (including CTEs)
• Connected back to Python using SQLAlchemy and pyodbc for analysis

🗃️ SQL Server + Python Integration Project

This project demonstrates how to use Microsoft SQL Server for database operations and connect it with Python for data extraction, manipulation, and analysis.

📂 Files Included

📥 Download Sample Output

You can download the result CSV directly here:

• SQLQuery1_2.sql
  Contains SQL scripts for:

  • Creating database/tables
  • Performing CRUD operations
  • Executing queries
  • Common Table Expressions (CTEs)

• Python+SQL.ipynb
  A Jupyter Notebook showcasing:

  • Connecting Python to MS SQL Server
  • Running SQL queries using Python (via pyodbc or similar)
  • Fetching and analyzing data using Pandas

⚙️ Technologies Used

• MS SQL Server
• SQL Server Management Studio (SSMS)
• Python 3.x
• Jupyter Notebook

• 🐍 Python Libraries:

• sqlalchemy
• pyodbc
• pandas

🚀 How to Use

1. Open and run SQLQuery1_2.sql in SSMS to create and populate the database.
2. Ensure your SQL Server instance is running and accessible.
3. Open Python+SQL.ipynb in Jupyter Notebook.
4. Update the connection string with your SQL Server credentials.
5. Run the notebook cells to execute and analyze SQL queries from Python.

📈 Project Objectives

• Practice SQL querying in MS SQL Server.
• Integrate SQL database with Python for analytics.
• Demonstrate real-time database interactions from Python.

📌 Notes

• You may need to install required Python libraries using:

  pip install pyodbc pandas sqlalchemy