Building a Scalable Real-Time Weather Dashboard with Python, OpenWeather API, and AWS S3

This document describes a Python project that retrieves weather data and stores it in an AWS S3 bucket. Let's rephrase it for clarity and improved flow, maintaining the original language and image positions.

Weather Dashboard Project

This Python project, the Weather Dashboard, retrieves weather data via the OpenWeather API and securely uploads it to an AWS S3 bucket. It provides a straightforward interface for viewing weather information for various cities and seamlessly saves the results to the cloud. The project's scalability is enhanced by leveraging AWS S3 for data storage.

Table of Contents

• Prerequisites
• Project Overview
• Core Functionality
• Technologies Used
• Project Setup
• Environment Configuration
• Running the Application

Prerequisites

Before starting, ensure you have:

1. Python 3.x: Download and install from the official Python website.
2. AWS Account: Create an account to access AWS S3.
3. OpenWeather API Key: Obtain a key from the OpenWeather website.
4. AWS CLI: Download and install the AWS Command Line Interface.
5. Python Proficiency: Basic understanding of Python scripting, API interaction, and environment variables.
6. Code Editor/IDE: Use VS Code, PyCharm, or a similar development environment.
7. Git: Install Git for version control (available from the Git website).

Project Overview

This Weather Dashboard utilizes the OpenWeather API to fetch weather information for specified locations. This data is then uploaded to an AWS S3 bucket for convenient remote access. The system's design allows users to input different cities and receive real-time weather updates.

Core Functionality

• Retrieves weather data from the OpenWeather API.
• Uploads weather data to an AWS S3 bucket.
• Securely manages API keys and AWS credentials using environment variables.

Technologies Used

The project utilizes:

• Python 3.x: The primary programming language.
• boto3: The AWS SDK for Python, enabling interaction with AWS S3.
• python-dotenv: Facilitates secure storage and retrieval of environment variables from a .env file.
• requests: A streamlined HTTP library for making API calls to OpenWeather.
• AWS CLI: The command-line interface for managing AWS services (including key configuration and S3 bucket management).

Project Setup

Follow these steps to set up the project locally:

1. Create Project Directory Structure

<code>weather-dashboard/
├── src/
│ ├── __init__.py
│ └── weather_dashboard.py
├── .env
├── tests/
├── data/
├── .gitignore
└── README.md</code>

Create the directories and files using these commands:

<code class="language-bash">mkdir weather_dashboard_demo
cd weather_dashboard_demo
mkdir src tests data</code>

2. Create Files

Create the necessary Python and configuration files:

<code class="language-bash">touch src/__init__.py src/weather_dashboard.py
touch requirements.txt README.md .env</code>

3. Initialize Git Repository

Initialize a Git repository and set the main branch:

<code class="language-bash">git init
git branch -M main</code>

4. Configure .gitignore

Create a .gitignore file to exclude unnecessary files:

<code class="language-bash">echo ".env" >> .gitignore
echo "__pycache__/" >> .gitignore
echo "*.zip" >> .gitignore</code>

5. Add Dependencies

Add required packages to requirements.txt:

<code class="language-bash">echo "boto3==1.26.137" >> requirements.txt
echo "python-dotenv==1.0.0" >> requirements.txt
echo "requests==2.28.2" >> requirements.txt</code>

6. Install Dependencies

Install the dependencies:

<code>weather-dashboard/
├── src/
│ ├── __init__.py
│ └── weather_dashboard.py
├── .env
├── tests/
├── data/
├── .gitignore
└── README.md</code>

Environment Configuration

1. AWS CLI Configuration

Configure the AWS CLI with your access keys:

<code class="language-bash">mkdir weather_dashboard_demo
cd weather_dashboard_demo
mkdir src tests data</code>

You'll be prompted for your Access Key ID, Secret Access Key, region, and output format. Obtain your credentials from the AWS Management Console (IAM > Users > Your User > Security Credentials).

Check the installation with:

<code class="language-bash">touch src/__init__.py src/weather_dashboard.py
touch requirements.txt README.md .env</code>

2. Configure .env

Create a .env file containing your API key and bucket name:

<code class="language-bash">git init
git branch -M main</code>

Replace placeholders with your actual values.

Running the Application

Here's the Python script (weather_dashboard.py):

<code class="language-bash">echo ".env" >> .gitignore
echo "__pycache__/" >> .gitignore
echo "*.zip" >> .gitignore</code>

1. Run the Script

Execute the script:

<code class="language-bash">echo "boto3==1.26.137" >> requirements.txt
echo "python-dotenv==1.0.0" >> requirements.txt
echo "requests==2.28.2" >> requirements.txt</code>

This fetches weather data and uploads it to your S3 bucket.

2. Verify S3 Bucket

Access your AWS S3 bucket to confirm the upload. Remember to delete the data afterward to avoid unnecessary charges.

This revised version maintains the original information while improving readability and flow. Remember to replace placeholder values with your actual API key and bucket name.

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