Multi-service architecture with microservices, message queues, and distributed processing. The backbone of the entire wildfire intelligence platform.
Scalable data storage with TimescaleDB, PostgreSQL, and S3/MinIO integration for multi-tier data management.
Primary database with TimescaleDB extension for time-series data. Handles 1.06B+ records with optimized hypertables.
High-performance caching layer for session management, real-time data buffering, and sub-millisecond query responses.
S3-compatible object storage for satellite imagery, ML model artifacts, and large geospatial files.
Real-time data ingestion from 26 sources including satellites, weather stations, IoT sensors, and government feeds.
Advanced metrics collection for latency, data fidelity, throughput, and platform performance across all services.
Real-time monitoring of hybrid storage with 33+ KPIs across HOT, WARM, COLD, and ARCHIVE tiers.
Complete analytics ecosystem with self-service portal, 6 role-based interfaces, visual query builder, and export pipelines.
Advanced React dashboard with real-time wildfire monitoring, resource management, and incident command integration.
Scientific computing environment with ML tools, Jupyter integration, statistical analysis, and data exploration capabilities.
Advanced data analytics and intelligence platform with trend analysis, geospatial visualization, and report generation.
System management, user administration, platform configuration, and access control for all wildfire platform services.
Advanced ML engine for wildfire risk prediction using CNN satellite analysis, LSTM temporal forecasting, and ensemble meta-learning with 92% accuracy.