Creating a Weather Dashboard with AWS S3 buckets and OpenWeather API

Achieving the Weather Dashboard Project

This is a comprehensive guide on how I successfully completed the Weather Dashboard project, utilizing Python, AWS S3, and the OpenWeather API. By following these steps, I learnt how to interact with APIs, set up a Python environment, configure AWS CLI, and handle files with AWS S3.

1. Create an OpenWeather Account and Obtain API Key
   Step:
   • I visited the OpenWeather website and created an account.
   • After logging in, I navigated to the API section and generated an API key. This key was used to fetch weather data for different regions.
   Lesson Learnt:
   • API Integration: I learnt how to create and use an API key to interact with external APIs (like OpenWeather). The API key is a critical part of securely accessing the data provided by the service.

2. Set Up My Work Environment in VSCode
   Step:
   • I created a new directory for the project and opened it in VSCode.
   • Inside the project directory, I created a .env file. This file will store sensitive information like the OpenWeather API key and the AWS S3 bucket name.
   o Example:
   

WEATHER_API_KEY=your_openweather_api_key
AWS_BUCKET_NAME=unique_bucket_name

Lesson Learnt:
• Environment Setup: I learnt how to use .env files to securely store sensitive data such as API keys. This is a good practice to avoid hardcoding sensitive information in my source code.

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1. Set Up AWS in My Terminal
   Step:
   • I logged into my AWS console and navigated to IAM (Identity and Access Management).
   • I created a new IAM user and attached the "AdministratorAccess" and "AmazonS3FullAccess" policies.
   • I generated an access key and secret key for the new IAM user.
   • In VSCode terminal, I ran aws configure and entered the Access Key ID and Secret Access Key when prompted.
   Lesson Learnt:
   • AWS CLI Configuration: I learnt how to configure AWS CLI using IAM credentials. This allows me to interact with AWS services (like S3) directly from my terminal using Python.

   ---

2. Set Up My Python Environment
   Step:
   • I downloaded and installed Python on my machine (Windows or Linux, based on my system).
   • I set up environment variables as needed.
   Lesson Learnt:
   • Python Setup: I gained experience in setting up a Python environment on my machine, including installing Python and configuring the system for running Python scripts.

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1. Folder Structure Setup Step: • I organized my project folder structure as follows: • weather-dashboard/ • `

`

├─ src/
    │   ├── __init__.py
    │   └── weather_dashboard.py
    ├── .env
    ├── .gitignore
    └── requirements.txt``

I added .gitignore to prevent sensitive files from being tracked by Git:

•

.env
• .git

• In the requirements.txt, I listed the necessary Python packages needed for the smooth run:
•

boto3==1.26.137
• python-dotenv==1.0.0
• requests==2.28.2

Lesson Learnt:
• Project Organization: I learnt how to structure a Python project to ensure clarity and maintainability. This also includes using. gitignore to keep sensitive files out of version control.

1. Install Required Python Packages
   Step:
   • I ran the following command to install the packages in requirements.txt:
   • python -m pip install -r requirements.txt
   Lesson Learnt:
   • Dependency Management: I learnt how to manage project dependencies with requirements.txt. This ensures all the required libraries are installed in one go.

2. Write the Script to Fetch and Store Weather Info
   Step:
   •
   

    I imported the required libraries:
• import requests
• import boto3
• from dotenv import load_dotenv
• import json
• import os

• I fetched weather data from the OpenWeather API and parsed it into JSON format.
• I created an S3 bucket and uploaded the weather data as a JSON file.
Lesson Learnt:
• API Data Handling: I learnt how to fetch and process data from an API and parse it into a format suitable for storage (JSON). This skill is essential for working with many types of external data sources.
• AWS S3 Integration: I learnt how to interact with AWS S3 to upload files from Python using boto3. This is crucial for cloud storage automation.

1. Run the Python Script
   Step:
   • After writing the script, I navigated to the src folder and ran the following command:
   • python weather_dashboard.py
   Lesson Learnt:
   • Testing and Debugging: I learnt the importance of testing my code regularly to ensure it works as expected. Running the script helped verify that all components were correctly integrated.

2. Final Check and Housekeeping
   Step:
   • After running the script, the terminal should show:
   o The S3 bucket is created successfully.
   o Weather data is displayed in the terminal and uploaded to the S3 bucket.
   • I went to my AWS console and checked the S3 bucket to confirm the presence of the uploaded files.
   • Once verified, I emptied the bucket and deleted it to prevent unnecessary charges.
   Lesson Learnt:
   • Resource Cleanup: I learnt the importance of cleaning up cloud resources (like S3 buckets) after use to avoid unexpected costs. This is an important best practice when working with cloud services.

Summary of Lessons Learnt

1. Working with APIs: I learnt how to fetch data from an external API (OpenWeather) and parse it into a format usable by my application.
2. Environment Setup: I gained experience in setting up a Python development environment, including managing sensitive data with .env files.
3. AWS CLI Configuration: I learnt how to configure AWS CLI with IAM credentials, allowing me to interact with AWS services such as S3 directly from the terminal.
4. Folder Structure and Project Organization: I understood the importance of structuring my project correctly and using. gitignore to keep sensitive data secure.
5. Python Package Management: I installed and managed dependencies using pip and requirements.txt to ensure my project runs smoothly.
6. AWS S3 Integration: I learnt how to use the boto3 library to interact with AWS S3, including creating buckets and uploading files.
7. Error Handling and Debugging: I gained skills in debugging common issues like missing credentials or AWS permission errors.
8. Resource Cleanup: I learnt to clean up AWS resources after use to avoid unnecessary billing and to keep the project efficient.

By completing this project, I gained hands-on experience working with APIs, AWS S3, and Python, and learnt key cloud computing practices. I’m now equipped with the knowledge to integrate and automate cloud-based services in my future projects!