gonzalomelov/dw-air-quality-platform

Business Intelligence System for Air Quality Data Analysis of the Montevideo Municipality

Air Quality Monitoring System for Montevideo

Introduction

Montevideo, characterized by its favorable climate and geographical conditions for natural pollutant dispersion, faces challenges in air quality management due to emissions from diverse urban activities. This project develops a BI tool to automate data extraction, integration, cleaning, and analysis concerning Montevideo's air quality.

Project Scope

Objectives

• Data Integration: Merge data from various sources relevant to air quality management.
• User Interface: Develop an intuitive interface for ongoing data uploads to the Data Warehouse (DW).
• Analytical Tools: Implement custom BI tools for extensive data analysis, including OLAP, dashboards, and geographic data visualization.
• User Experience: Ensure the system is user-friendly and meets the analytical needs of its users.

Expected Outcomes

• Deploy a functional BI tool prototype.
• Complete comprehensive project documentation and a user manual.
• Equip students with practical knowledge on BI system development.

System Architecture

This section describes the high-level architecture of the air quality monitoring system, including data sources, ETL processes, DW design, and end-user interfaces.

Components

• Data Sources: Include sensors from the air quality monitoring network, government databases, and historical records.
• ETL Layer: Automated scripts for data extraction, transformation, and loading.
• Data Warehouse: A centralized repository designed to support query and analysis.
• BI Tools: Custom applications for data analysis, including OLAP cubes and interactive dashboards.

Installation

Prerequisites

• Docker
• Python 3.8+
• PostgreSQL

Steps

1. Clone the repository:

git clone https://github.com/your-repository/air-quality-monitoring.git

2. Navigate to the project directory:

cd air-quality-monitoring

3. Build and run the Docker containers:

docker-compose up --build

Usage

Data Upload

1. Log into the admin panel.
2. Navigate to 'Data Sources' and click 'Upload Data'.
3. Select the data file and specify the data source.

Analyzing Data

• Access the dashboard at http://localhost:8080/dashboard.
• Use filters to select data ranges, geographic areas, and other parameters.

Contributing

Issues

Feel free to submit issues and enhancement requests.

Contributing Guidelines

1. Fork the repo.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -am 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

Team

• Gonzalo Melo - [email protected]

Additional Information

Detailed bibliography and annexes are available in the 'docs' folder to complement the system documentation.