Automated-ETL-Pipeline-for-Weather-Data

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Automated-ETL-Pipeline-for-Weather-Data

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This project implements an Automated ETL (Extract, Transform, Load) Pipeline that fetches daily weather data, processes it, and stores it locally. It uses the OpenWeatherMap API to extract current weather data for a given city

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# Automated ETL Pipeline for Weather Data

## Overview
This project is an **Automated ETL (Extract, Transform, Load) Pipeline** designed to collect, process, and store weather data from various sources. The pipeline ensures seamless data integration for analytics, reporting, and visualization.

## Features
- **Automated Data Extraction**: Retrieves weather data from APIs, CSV files, or databases.
- **Data Transformation**: Cleans, formats, and enriches the data to ensure consistency.
- **Efficient Data Loading**: Stores the processed data into a database or a cloud storage solution.
- **Scheduled Execution**: Uses cron jobs or workflow orchestration tools for automation.
- **Logging & Monitoring**: Tracks data processing stages and errors.

## Technologies Used
- **Python**: Main scripting language for ETL tasks.
- **Pandas**: Data manipulation and transformation.
- **SQL / PostgreSQL**: Database storage.
- **Apache Airflow / Prefect**: Workflow orchestration.
- **APIs / Web Scraping**: For data extraction from online sources.
- **AWS S3 / Google Cloud Storage** (Optional): Cloud-based data storage.

## Installation
### Prerequisites
- Python 3.x installed
- PostgreSQL or any other database service (optional)
- Required Python packages (listed in `requirements.txt`)

### Setup
1. Clone the repository:
   ```sh
   git clone https://github.com/yourusername/Automated-ETL-Pipeline-for-Weather-Data.git
   cd Automated-ETL-Pipeline-for-Weather-Data
   ```
2. Install dependencies:
   ```sh
   pip install -r requirements.txt
   ```
3. Configure the `.env` file with API keys, database credentials, and other configurations.

## Usage
Run the ETL script manually:
```sh
python main.py
```
Or schedule the pipeline using Airflow, cron jobs, or Prefect.

## Project Structure
```
Automated-ETL-Pipeline-for-Weather-Data/
│-- src/
│   │-- extract.py       # Handles data extraction
│   │-- transform.py     # Cleans and processes data
│   │-- load.py          # Stores processed data
│   │-- config.py        # Configuration settings
│-- main.py              # Main execution script
│-- requirements.txt     # Dependencies
│-- README.md            # Project documentation
```

## Contributing
Feel free to contribute by opening an issue or submitting a pull request.

## License
This project is licensed under the MIT License.

## Contact
For any inquiries, reach out via **akajiakuflowz@gmail.com**

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