Kaggle Data Fetch and PostgreSQL Population

This script fetches data from a Kaggle endpoint, extracts it from a zip file, and populates a PostgreSQL database with the extracted data.

Prerequisites

• Python 3.x installed on your machine.
• PostgreSQL server installed and running locally or accessible remotely.
• Necessary Python packages installed (requests, pandas, sqlalchemy).

Setup

1. Clone the repository or download the script file.

2. Install the required Python dependencies using pip:

   pip install requests pandas sqlalchemy

3. Ensure PostgreSQL server is running and configure connection parameters (DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME) in the script according to your setup.

Usage

1. Run the script populate.py using Python:

   python populate.py

2. The script will:

   • Fetch the data from the Kaggle endpoint specified (KAGGLE_ZIP_URL).
   • Extract the data from the downloaded zip file into memory.
   • Populate a PostgreSQL database named Email_db (change as per your database name) with the extracted data assuming it's in CSV format.

Script Explanation

• fetch_and_extract_data(): Fetches the data from KAGGLE_ZIP_URL, extracts it into memory from the zip file, and returns it.

• populate_db(data): Establishes a connection to the PostgreSQL database using SQLAlchemy, reads the extracted data (assuming CSV format), and populates a table named ClientData (replace with your desired table name) with the data.

Configuration

• Database Connection Parameters (DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME): Modify these variables in the script to match your PostgreSQL database connection details.

• KAGGLE_ZIP_URL: Replace this variable with the actual Kaggle endpoint URL from where you want to fetch the data.

Notes

• Ensure the PostgreSQL user (DB_USER) has appropriate privileges to create and modify tables in the specified database (DB_NAME).

• This script assumes the fetched data is in CSV format. Adjust the pd.read_csv() function if your data format is different.

Error Handling

• The script includes basic error handling to manage exceptions during data fetching, extraction, and database population. Errors will be printed to the console with appropriate messages.