Building a Cloud-Based Weather Dashboard: Day 1 of My 30 Days DevOps Challenge

As part of the 30 Days DevOps Challenge from Cozy Cloud Crew, I started on a journey to refine my cloud (specially AWS) and DevOps skills. For Day 1, the challenge was to build a Python-based Weather Dashboard that fetches real-time weather data and securely stores it in an AWS S3 bucket. This blog will take you through my project, challenges, and learnings from Day 1.

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Project Overview

The Weather Dashboard app retrieves real-time weather data for any city using the OpenWeatherMap API. It displays the weather data in a clean GUI built with Tkinter and saves the raw data in JSON format to an AWS S3 bucket for long-term storage or analysis.

Core Features

• Data Fetching: Retrieves current weather details using OpenWeatherMap API.

• User-Friendly Interface: A graphical interface for input and display.

• Cloud Integration: Stores fetched weather data in an AWS S3 bucket via the Boto3 SDK.

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Tech Stack

• Python: Core programming language.

• Tkinter: For creating the GUI.

• Boto3: AWS SDK for Python to interact with S3.

• dotenv: To manage sensitive credentials.

• OpenWeatherMap API: For real-time weather data.

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Project Structure

The project was modular, organized as follows:

weather-dashboard/
├── src/                 # Source code for the application
│   ├── __init__.py      # Makes src a package (can be empty)
│   ├── gui.py           # Tkinter GUI code
│   ├── weather_api.py   # Weather API interaction logic
│   └── s3_utils.py      # S3 bucket interaction logic
├── tests/               # Unit tests for the application
│   ├── __init__.py     
│   ├── test_gui.py
│   ├── test_weather_api.py
│   └── test_s3_utils.py
├── .gitignore           # Files/folders to exclude from Git
├── requirements.txt     # Project dependencies
├── README.md            # Project description and instructions
└── main.py              # Entry point of the application

Checkout the Github Repo:

Check out the Day 1 code here : Day 1 - Weather Dashboard

Architecture

Fun fact: I had bit of time so I also learned to create Mermaid diagrams, it was a fun activity. Below is the outcome of my learning.

graph LR
    subgraph User_Interaction
        A[User] -.-> B{Enter City}
    end

    subgraph Weather_Application
        M[main.py] --> C
        M[main.py] --> E
        M[main.py] --> G
        B{Enter City} --"City Name"--> C[weather_api.py]
        C[weather_api.py] --"API Request"--> D[OpenWeather API]
        D[OpenWeather API] --"Weather Data"--> C
        C --"Formatted Data"-->  E[Weather Dashboard GUI]
        E[Weather Dashboard GUI] --> F[Display Weather]
    end

    subgraph Data_Storage
        C[weather_api.py] -- "Weather Data" --> G[s3_utils.py]
        G[s3_utils.py] --"Store Data"--> H[AWS S3]
    end

    F[Display Weather] -.-> A[User]

    %% Styling for better readability
    style A fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px,font-size:16px,color:#1b5e20
    style B fill:#ffffff,stroke:#2e7d32,stroke-width:1px,font-size:16px,color:#000000
    style C fill:#e1f5fe,stroke:#01579b,stroke-width:2px,font-size:16px,color:#000000
    style D fill:#f5f5f5,stroke:#616161,stroke-width:1px,font-size:16px,color:#000000
    style E fill:#e1f5fe,stroke:#01579b,stroke-width:2px,font-size:16px,color:#000000
    style F fill:#f5f5f5,stroke:#616161,stroke-width:1px,font-size:16px,color:#000000
    style G fill:#e1f5fe,stroke:#01579b,stroke-width:2px,font-size:16px,color:#000000
    style H fill:#f5f5f5,stroke:#616161,stroke-width:1px,font-size:16px,color:#000000
    style M fill:#e1f5fe,stroke:#01579b,stroke-width:2px,font-size:16px,color:#000000

Learnings and Reflections

This project gave me hands-on experience with cloud and DevOps principles. Here are my key takeaways:

1. Managing Secrets Securely:
   Storing API keys and AWS credentials in a .env file ensured sensitive information wasn’t exposed in the codebase. Using dotenv, I dynamically loaded these variables at runtime.

2. Cloud Storage Basics:
   While I had prior experience with AWS, integrating an application to dynamically interact with S3 gave me insights into API-first design.

3. GUI Development with Tkinter in Python:
   Building a graphical interface taught me how to enhance usability while adhering to project requirements.

4. Writing Unit Tests:
   Testing each module independently was a valuable learning experience. It reinforced the importance of modular design and highlighted how unit tests ensure reliability. For example, I tested API responses with mock data and verified S3 interactions using a test bucket.

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Challenges and Troubleshooting

The issues and errors I faced and troubleshooted:

1. IllegalLocationConstraintException

• Problem: When creating an S3 bucket, I encountered this error due to an unspecified or mismatched region.

• Solution: Updated boto3.client() to include the appropriate region:

    self.s3 = boto3.client('s3', region_name='ap-south-1')
  

2. NoSuchBucket Error

• Problem: This error occurred when trying to save data to a non-existent S3 bucket.

• Solution: I added a function to verify bucket existence and ensure creation:

    def create_bucket_if_not_exists(bucket_name):
        if bucket_name not in existing_buckets:
            s3.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={'LocationConstraint': 'ap-south-1'})
  

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Outcome

The final app successfully fetched weather data, displayed it on the GUI, and saved it to S3. Here’s a snapshot of the GUI:

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Final Thoughts

This project laid a strong foundation for integrating Python apps with cloud services. It gave me a glimpse into real-world challenges, from handling API responses to troubleshooting AWS configuration issues. Most importantly, it solidified my belief that hands-on projects are the best way to learn DevOps.

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🚀 What's Next?

Next up, I’ll be working on a Game Day Notification System. Stay tuned as I continue this challenge, building new projects and documenting my journey.

If you’re curious, feel free to check out the project repository: Weather Dashboard Repo