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Building a Weather Dashboard App with Docker, Flask, Open Weather API and AWS S3

Hey builders! In this article, we will be going through the contents of a github repository that uses Docker to run a weather dashboard application. This application uses Open Weather API to fetch real-time weather data for multiple…

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Hey builders! In this article, we will be going through the contents of a github repository that uses Docker to run a weather dashboard application. This application uses Open Weather API to fetch real-time weather data for multiple cities, and utilizes AWS s3 for secure and scalable data storage.



Python was used for scripting, AWS for cloud management.



This project underscores the principles of modern DevOps including automation, cloud efficiency and error handling.



Here's the github repository for the project, check here



Let's dive in!









Prerequisites:








Repository Structure



Here is an overview of the repository structure:



Image description









Key Files and Their Purpose:



1. web_dashboard.py

This is the main application file. It contains the WeatherDashboard class, which handles fetching weather data from the OpenWeather API and storing it to an AWS S3 bucket.



The class also includes methods for creating the S3 bucket if it doesn't exist and saving weather data to the bucket.



2. .env



This file contains environment variables required by the application, such as the OpenWeather API key and AWS credentials. It ensures sensitive information is not hard-coded into the application.



Image description



**3. Dockerfile



The Dockerfile defines the steps to build a Docker image for the application. It installs the necessary dependencies, copies the application code, and sets the command to run the application.



Image description









Setting Up the Application







  1. Clone the repo:





    git clone https://github.com/NonsoEchendu/flask-weather-dashboard.git





  2. Change directory





    cd 30days-weather-dashboard





  3. Create a .env file and place it in the project's root directory:





    OPENWEATHER_API_KEY=your=openweather-api-key
    AWS_BUCKET_NAME=your-aws-bucket-name
    AWS_BUCKET_FOLDER_NAME=your-aws-bucket-folder-name
    AWS_ACCESS_KEY_ID=your-aws-access-key-id
    AWS_SECRET_ACCESS_KEY=your-aws-secret-access-key
    AWS_DEFAULT_REGION=your-aws-default-region





  4. Build the docker image





    docker build -t weather-dashboard .





  5. Run the docker container:





    docker run --env-file .env -p 5000:5000 -d --name weather-dash weather-app







-p 5000:5000 maps the container's port 5000 to your local machine's port 5000, so you can be able to view it on your web broswer





  1. Open http://localhost:5000 on your web browser. You should have something like this when you search for a city:




Image description










Conclusion



By using docker to deploy this application, we are implementing core devops principles: containerization, automation. So instead of installing each of those tools locally, with just 2 commands we get our application running.



Future growth for this project will be seting up CI/CD pipeline for automation.






Happy building and Collaborating!

SOC Incident Playbook: Vulnerability Remediation & Verification
title: Detect Exploitation - Building a Weather Dashboard App with Docker, Flask, Open Weather API and AWS S3
id: 7fcae0b9-f82f-4c56-a673-ff596defce0d
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "Building a Weather Dashboard A" ascii wide
    condition:
        any of them
}
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich Building a Weather Dashboard App with Do.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

Empfohlene Sofortmaßnahmen
  • 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
  • 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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