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Development of Scalable Environmental Data Integration and Visualization Platform

themomentum.ai

Environmental Services

Government

Information technology

Urban Environmental Data Management Challenges

Need to unify and analyze massive volumes of heterogeneous environmental sensor data in real-time while maintaining scalability, ensuring data accuracy, and providing actionable insights through intuitive visualization tools for policymakers and researchers.

About the Client

Global smart city solutions provider specializing in air quality monitoring and environmental data analytics

Platform Development Goals

Create unified data architecture for multi-source environmental sensor integration
Implement real-time air quality monitoring capabilities
Develop intuitive data visualization tools for policymakers
Establish historical data analysis functionality
Ensure system scalability for global expansion

Core System Capabilities

Multi-format data ingestion pipeline
Real-time air quality dashboard with sensor status tracking
Advanced analytics engine with data aggregation
Historical data visualization and trend analysis
Configurable data download scheduling
IoT device health monitoring interface

Technology Stack

Ruby on Rails

PostgreSQL

Redis

Sidekiq

System Integrations

Third-party environmental sensor APIs
Geospatial mapping services
Government environmental databases
Cloudflare Bot Management

Performance Requirements

Real-time processing of 100,000+ API requests
High-availability data pipeline with fault tolerance
Sub-second dashboard response times under heavy load
Secure data handling with GDPR compliance
Horizontal scalability for global sensor network expansion

Expected Environmental and Operational Impact

Enables cities to identify pollution sources 70% faster through real-time data visualization, reduces environmental response times through proactive monitoring, and supports evidence-based policy decisions with accurate historical trend analysis while maintaining system performance during exponential data growth.