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// LIVE OVERVIEW MAP — REAL-TIME DATA
Every second, Pandita Data's Risk Brain processes millions of hazard signals across the planet—earthquake tremors, atmospheric pressure shifts, thermal anomalies, magnetic fluctuations. But raw data is noise. What transforms chaos into life-saving intelligence is architecture. This is the story of how six interconnected stages turn global sensor streams into the AI risk scores you see live on the Brain Dashboard.
THE 6-STAGE DATA PIPELINE
The journey from sensor to your screen follows a relentless sequence designed for speed, accuracy, and resilience. Each stage is a guardian—filtering, enriching, scoring, storing, serving, and finally visualizing the most critical hazard intelligence on Earth.
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STAGE 1: INGEST
Real-time feeds from 500+ seismic networks, weather APIs, satellite thermal sensors, and magnetometer arrays stream into distributed message queues. Rate-limited by source reliability.
Sub-1s latency
⚙️
STAGE 2: PARSE
Heterogeneous formats (seismic XML, METAR, HDF5 satellite) are standardized into a unified hazard schema. Geolocation validation and unit normalization occur here. Bad records are tagged for review.
Multi-format support
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STAGE 3: SCORE
Parsed signals enter ensemble ML models tuned for earthquake, tsunami, wildfire, volcanic, flood, weather, magnetic, and aurora risk. Physics-informed neural networks cross-validate hazard probability, severity, and timeline.
8 hazard types
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STAGE 4: STORE
Scored hazard events and city-level AI risk indices are persisted to a time-series database with 10-year retention. Alongside: raw signals, model versions, and audit logs for reproducibility.
10yr history
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STAGE 5: SERVE
Risk scores and drill-down data are cached in a hierarchical CDN layer (global → regional → city). JSON payloads are pre-rendered for 3D sim loads and live dashboard queries.
~200ms P99
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STAGE 6: DISPLAY
The Brain Dashboard at panditadata.com/brain_dashboard renders live risk cards, 3D hazard layers, and city-level Disaster Reports. Each viewport updates on signal ingestion.
Real-time viz
GRACEFUL DEGRADATION & RESILIENCE
What happens when a sensor fails? When a model times out? When a region loses connectivity? Pandita's pipeline does not crash—it degrades intelligently.
90s
Max timeout per city query. After 90 seconds, cached data (with stale flag) serves instead.
2h
Data age threshold. After 2 hours without fresh signals, the Brain marks risk scores with a stale flag in the Brain Dashboard.
99.2%
Pipeline uptime SLA across all six stages. Redundant workers at Stages 2, 3, and 5 eliminate single points of failure.
4
Fallback scoring modes. If ensemble models fail, physics-rule-based risk heuristics activate for all 8 hazard types.
THE JSON CACHE ARCHITECTURE
How Scores Reach Your Browser in ~200ms
After Stage 5 (SERVE), the Brain Dashboard subscribers receive immutable JSON snapshots every 30 seconds. These payloads contain city-level risk indices for all 8 hazard types, model confidence intervals, and pointers to live 3D simulations. A second cache layer at the edge CDN ensures zero re-computation during traffic spikes. This is why your Disaster Report (panditadata.com/disaster
🧠 OPEN BRAIN DASHBOARD
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