ClimaInsights is an automated ETL (Extract, Transform, Load) pipeline designed to collect, process, and store daily weather data from multiple cities. Using Apache Airflow, Python, and the Weatherstack API, this project ensures efficient, scalable, and error-free weather data retrieval for various applications.
Accurate and timely weather data is essential for industries like agriculture, logistics, and disaster management. However, current methods for collecting and processing weather data are:
- Manual & Error-Prone: Many organizations still rely on manual data collection, leading to inconsistencies and inaccuracies.
- Lacking Scalability: As data requirements grow, existing systems struggle to handle large-scale, multi-city weather data efficiently.
- Inefficient Decision-Making: Delays and errors in weather data processing affect strategic planning and real-time applications.
A well-structured ETL pipeline can solve these challenges by automating data collection, ensuring data integrity, and providing a scalable infrastructure for weather analytics. ClimaInsights leverages data engineering best practices to streamline the entire process.
- Automate Data Collection: Fetch daily weather data from multiple cities using the Weatherstack API.
- Data Processing & Cleaning: Use Python scripts to clean and standardize raw weather data.
- Efficient Data Storage: Store processed weather data in a structured database for easy access and analysis.
- Data Orchestration: Manage and schedule ETL workflows using Apache Airflow.
- Error Handling & Logging: Implement robust logging and error-handling mechanisms for reliability.
- Automation & Efficiency – Eliminates manual work, reducing errors and improving data accuracy.
- Scalability – Handles growing data volumes and additional cities seamlessly.
- Optimized Resource Utilization – Frees up data engineers to focus on strategic insights rather than repetitive tasks.
- Python – Data extraction, transformation, and processing
- Apache Airflow – Workflow orchestration
- Weatherstack API – Real-time weather data retrieval
- PostgreSQL / MySQL – Structured data storage
- Python 3.x
- Apache Airflow
- Weatherstack API Key
- Database (PostgreSQL or MySQL)
- Clone this repository:
git clone https://github.com/your-username/ClimaInsights.git cd ClimaInsights - Install dependencies:
pip install -r requirements.txt
- Set up Weatherstack API Key in the environment variables.
- Configure and start Apache Airflow for ETL orchestration.
Contributions are welcome. Feel free to submit a pull request or open an issue for suggestions and improvements.