Python AWS Weather Data Pipeline Guide

What I Built

#

I created a Python script that automates fetching weather data for 5 cities (London, New York, Amsterdam, Delhi, Oslo) from the OpenWeather API, processes it, saves it locally, and uploads it to an AWS S3 bucket (cloud storage). Think of it as a weather data factory:

1. Fetch raw data from OpenWeather.
2. Process it to keep only temperature, humidity, and weather conditions.
3. Save locally as JSON files.
4. Upload to the cloud (AWS S3) for safekeeping.

Architecture Diagram

#

How I Approached It

#

I broke the problem into small, manageable tasks and tackled them one by one. Here’s my roadmap:

1. Authentication & Setup

#

• Problem: API keys and AWS credentials are sensitive!
• Solution: Use .env files to store secrets (never hardcode them!).

  # Load secrets from .env
    load_dotenv()
    api_key = os.getenv("API_KEY")
    bucket_name = os.getenv("S3_BUCKET_NAME")
  

2. Fetch Data from OpenWeather

#

• Problem: How to get live weather data?
• Solution: Use Python’s requests library to call the API.

  def fetch_weather_data(api_key, city):
        base_url = "https://api.openweathermap.org/data/2.5/weather"
        params = {"q": city, "appid": api_key}
        response = requests.get(base_url, params=params)
        return response.json()
  

3. Process the Data

#

• Problem: The API returns 50+ fields—I only need 4!
• Solution: Extract relevant data using a dictionary.

  def extract_relevant_data(data):
        return {
            "name": data.get("name"),
            "description": data["weather"][0]["description"],
            "temp": data["main"]["temp"],
            "humidity": data["main"]["humidity"]
        }
  

4. Save Locally

#

• Problem: Organize files by city name.
• Solution: Create a weather_data folder and save JSON files.

  def save_to_local(data, city):
        directory = "weather_data"
        os.makedirs(directory, exist_ok=True)  # Create folder if missing
        file_path = os.path.join(directory, f"{city}_weather.json")
        with open(file_path, "w") as file:
            json.dump(data, file, indent=4)
  

5. Upload to AWS S3

#

• Problem: Ensure the S3 bucket exists; handle errors.
• Solution: Check for the bucket, create it if missing, then upload.

  def bucket_exists(client, bucket_name):
        try:
            client.head_bucket(Bucket=bucket_name)
            return True
        except Exception as e:
            print(f"Error: {e}")
            return False
    def upload_to_s3(client, bucket_name, file_path, s3_key):
        client.upload_file(file_path, bucket_name, s3_key)
  

Why I Chose Procedural Programming (Not OOP)

#

I structured the code as a series of functions (procedural style) instead of using classes (object-oriented programming). Here’s why:

1. Simplicity

#

• The script is linear: Fetch → Process → Save → Upload.
• Example:

  def main():
        # Step 1: Connect to AWS
        client = boto3.client('s3')
        # Step 2: Check bucket
        if not bucket_exists(client, bucket_name):
            create_bucket(client, bucket_name)
        # Step 3: Process cities
        for city in cities:
            data = fetch_weather_data(...)
            save_to_local(...)
            upload_to_s3(...)
  

  This reads like a recipe—easy for beginners to follow!

2. Scope

#

• The script does one thing: move data from Point A (API) to Point B (S3).
• No need for complex class hierarchies.

3. Faster Prototyping

#

• Functions let me build and test individual parts quickly.

When Would I Use OOP?

#

If the project grew (e.g., adding a dashboard, user input, or multiple data sources), I’d switch to OOP. Example:

class WeatherPipeline:
    def __init__(self, api_key, bucket_name):
        self.api_key = api_key
        self.bucket_name = bucket_name

    def fetch_data(self, city):
        # ... logic here ...

    def upload_to_cloud(self, file_path):
        # ... logic here ...

Key Challenges & Solutions

#

1. Error Handling
   • What if the API is down?
   • Fix: Used try/except blocks to catch failures.

     try:
           response = requests.get(...)
           response.raise_for_status()  # Crash if API call fails
       except requests.exceptions.RequestException as e:
           print(f"API Error: {e}")
     

2. AWS Permissions
   • Fix: Configured IAM roles in AWS to grant S3 access.
3. Data Clutter
   • Fix: Used extract_relevant_data() to keep only what’s needed.

• Schedule the script to run daily (e.g., with AWS Lambda).
• Add a dashboard to visualize weather trends.
• Expand cities or integrate more APIs (e.g., weather forecasts).

Lessons for Beginners

#

1. Start small. Break projects into tiny tasks.
2. Secure secrets. Never commit API keys to GitHub!
3. Embrace functions. They keep code organized and reusable.

Happy coding! 🌟 Whether you’re automating weather data or building the next Netflix, remember: every big project starts with a single line of code.

The full project code and setup script are available on GitHub: Weather-Dashboard