The Challenge: Thousands of Assets, Real-Time Data

Eflex's customers own distributed energy assets: rooftop solar, warehouse batteries, grid-connected wind. Each asset generates data constantly — power output, temperature, voltage, efficiency metrics. Each asset can fail silently (generating 80% of normal output and nobody notices for months) or catastrophically (catch fire).

Andrew Winter's team needed:

• Real-time visibility: Dashboard showing live status of every asset across locations
• Failure detection: Alerts when performance drops, before customers notice
• Data reliability: Never lose a data point, even with network interruptions
• Scale: Thousands of assets reporting every minute, forever
• Mobile access: Field technicians need to see data and respond in the field

This isn't a typical web app. It's a real-time data pipeline. Data comes in from thousands of IoT devices, gets processed, stored, analyzed, and surfaced to operators — all with sub-second latency expectations.

The Scale Problem

Most traditional web architectures break at this scale. A REST API can't handle thousands of devices connecting simultaneously. A typical database struggles with the write throughput. You need different thinking.

We designed for:

• Connection efficiency: IoT devices don't use HTTP. They use MQTT (publish-subscribe, much lighter)
• Data pipelines: Stream processing, not request-response. Data flows through Kafka, gets aggregated, gets stored
• Time-series database: Traditional SQL databases are terrible at time-series data. We use InfluxDB for metrics
• Caching: Hot data stays in Redis. Historical data lives in cold storage
• Alerting: Rules engine evaluates streams in real-time, triggers alerts without database round-trips

The architecture: IoT devices publish data to MQTT brokers → Kafka topics → Stream processors → InfluxDB + PostgreSQL → Redis cache → Web API → Web dashboard + Mobile app. Each layer does one job, scales independently.

The Solution: A Real-Time Data Platform

Backend: Stream processing architecture using Kafka, Spark, and InfluxDB. Data ingestion is asynchronous and resilient — if the network hiccups, devices queue and retry.

Web Dashboard: Real-time map showing asset locations and status. Click on an asset, see its data history, performance curves, alerts. Built with React and WebSocket connections (not polling, which would kill the server).

Mobile App: iOS/Android app for field teams. See nearby assets, get alerts, log maintenance. Built native for performance (not a web wrapper).

Predictive Engine: ML models trained on historical asset data to predict failures. Not "asset X will fail tomorrow" — more like "asset X is degrading, expect 20% performance loss in 2 weeks unless maintained."

Predictive Maintenance: The Real Value

Raw real-time monitoring is valuable. But predictive maintenance is transformative. By analyzing patterns in device behavior — voltage sag, efficiency curves, temperature trends — our models can flag degradation before catastrophic failure.

Example: A solar inverter's efficiency drops from 97% to 95% over three weeks. Invisible to manual inspection. The model flags it: "Degradation detected, probability of failure in 30 days: 72%." Maintenance scheduled. Failure averted.

This required:

• Months of historical data from Eflex's existing systems
• Feature engineering (creating meaningful signals from raw metrics)
• Model training and validation
• Integration into the live data pipeline

By month 3, the predictive models were catching failures 2–3 weeks before they'd happen. That's the difference between planned maintenance (cheap) and emergency repair (expensive + downtime).

Operations & Reliability

A real-time data platform can't have downtime. If the dashboard goes down for 30 minutes, customers lose visibility into thousands of assets. We built for resilience:

• Multi-region deployment: Data pipelines run in multiple regions, with automatic failover
• Data replication: No single point of data loss
• Circuit breakers: If one component fails, the system degrades gracefully, not catastrophically
• Monitoring: Prometheus + Grafana watching every layer

Three years in, Eflex's platform has 99.97% uptime (less than 9 hours of downtime per year). That's not luck. That's disciplined engineering.