30-Days-Devops-Challenge

Building a Weather Data Collection System with DevOps Principles

Badmus Faoziyat's photo
Badmus Faoziyat
·

2 min read

In today’s fast-paced world, weather data is more than just a convenience; it’s a necessity for industries ranging from agriculture to logistics. As a DevOps engineer, I recently built a Weather Data Collection System that leverages core DevOps principles to streamline data collection and storage. This project highlights the fusion of cloud computing, external API integration, and Python development.

Project Overview

The Weather Data Collection System integrates:

  • External API Integration: OpenWeather API to fetch real-time weather data.

  • Cloud Storage: AWS S3 for secure and scalable data storage.

  • Infrastructure as Code (IaC): To ensure reproducibility and scalability.

  • Version Control: Git for seamless collaboration and code management.

  • Python Development: For API interaction and data processing.

  • Error Handling: Robust mechanisms for handling exceptions and failures.

  • Environment Management: Secure management of sensitive keys and configurations.

Key Features

  • Fetches real-time weather data for multiple cities.

  • Displays temperature (°F), humidity, and weather conditions.

  • Automatically stores weather data in AWS S3.

  • Tracks multiple cities with time-stamped records for historical analysis.

Technical Architecture

  • Language: Python 3.x

  • Cloud Provider: AWS (S3)

  • External API: OpenWeather API

  • Dependencies:

    • boto3: AWS SDK for Python

    • python-dotenv: For environment variable management

    • requests: For API requests

Project Structure

weather-dashboard/
  src/
    __init__.py
    weather_dashboard.py
  tests/
  data/
  .env
  .gitignore
  requirements.txt

Setup Instructions

  1. Clone the repository:

     git clone https://github.com/Faoziyah/30_days_devops_challenge.git

  2. Install dependencies:

     pip install -r requirements.txt

  3. Configure environment variables (.env):

     OPENWEATHER_API_KEY=your_api_key
 AWS_BUCKET_NAME=your_bucket_name

  4. Set up AWS credentials:

     aws configure

  5. Run the application:

     python src/weather_dashboard.py

Lessons Learned

  • AWS S3: Mastered bucket creation and management.

  • Environment Management: Leveraged .env files for secure API key storage.

  • Python Best Practices: Improved API integration and error handling skills.

  • Git Workflow: Streamlined collaboration through effective version control.

  • Cloud Resource Management: Gained insights into scalable and cost-effective solutions.

Future Enhancements

  • Add weather forecasting.

  • Implement data visualization tools.

  • Expand to track more cities.

  • Introduce automated testing frameworks.

  • Set up a CI/CD pipeline for continuous integration and deployment.

Conclusion

This project exemplifies how DevOps principles can transform a simple idea into a robust, scalable system. By integrating cloud solutions, version control, and Python development, I’ve created a system that’s not only functional but also a testament to the power of modern software development practices. Stay tuned for updates as I enhance this project with new features and capabilities!

DevOpsAllStarsChallengeAWSDevops