Florida Power & Light (FPL), a subsidiary of NextEra Energy, operates one of the most extensive and complex energy infrastructures globally. With 13 million sensors transmitting 140 billion data points per day across 400+ sites, FPL needed a robust IoT data pipeline to support grid optimization, predictive maintenance, AI-driven analytics, and renewable integration. HiveMQ became the backbone for real-time data ingestion and intelligence across the entire energy operations ecosystem.
Modernizing Grid Data Infrastructure for Real-Time Intelligence
Operating a modern energy grid requires high-volume, high-frequency data collection from distributed assets, often in challenging environments. FPL faced several obstacles with its traditional data pipelines which were not designed for hyperscale, high-frequency IoT telemetry from millions of sensors across hundreds of energy sites. The systems struggled to maintain performance and avoid data loss at industrial scale.
In order to make immediate and critical business decisions, FPL needed low latency data delivery and real-time processing of sensor data from substations, smart meters, generation plants, renewable sources, and field devices. They required guaranteed message delivery and fault tolerance across distributed infrastructure to maintain continuous operations at all sites. The new architecture needed to reliably move data from GE Proficy collectors into cloud processing systems like Databricks, forming the backbone of edge-to-cloud analytics.
The ultimate goal was operational continuity, as FPL is required to support NextEra Energy’s operational requirements and any data gaps or delays could impact power generation and distribution decisions across the energy infrastructure. As a result, FPL set out to adopt an extremely reliable, secure, event-driven data platform to power real-time operations and AI.
Building a Reliable, Edge-to-Cloud Data Pipeline with HiveMQ
FPL deployed the HiveMQ Platform as the real-time data streaming layer for its entire operational data ecosystem. HiveMQ serves as the messaging backbone receiving MQTT messages from data loggers and sending them to downstream systems like Databricks with sub-second latency.
They chose HiveMQ for horizontally scalable clustering, persistent sessions to handle collector disconnects without data loss, and message queuing with configurable retention policies for gap-free delivery. HiveMQ retains messages during network outages at edge locations, and automatically republishes buffered data when connectivity resumes, maintaining chronological ordering even when messages arrive late.
“HiveMQ solved our critical gaps, eliminating data loss during collector failovers,” said Murthy Manchala, Solution Architect at FPL. “The implementation became essential for maintaining the gap-free, real-time data pipeline our operational systems depend on.”
HiveMQ ensures zero data loss, extreme availability, and horizontal scalability. Most importantly it handles massive scale, with real-time flow of 140 billion data points daily from 13 million sensors, processing MQTT topics from 400+ sites simultaneously.
With HiveMQ, FPL created a unified, edge-to-cloud pipeline that supports mission-critical energy operations and ensures operational continuity.
Delivering Mission-Critical Reliability, Cost Efficiency, and AI Readiness
FPL reports that its HiveMQ implementation delivered the mission-critical reliability required for their operational IoT infrastructure while significantly improving cost efficiency and system performance. Cost optimization includes a 40% reduction in Databricks DBU costs through optimized message batching and routing.The company also eliminated expensive data recovery procedures and reduced operational overhead with centralized MQTT topic management.
“The business impact of HiveMQ includes operational continuity across all NextEra Energy sites, with real-time decision-making capabilities for critical power infrastructure,” said Manchala. “The scalable foundation we built supports future IoT expansion even beyond our current 13 million sensors, and does so with reduced risk of data gaps that could impact power generation operations.”
The architecture simplifies integration by eliminating complexity points, including replacing GE Proficy’s native WebSocket constraints with more reliable MQTT Sparkplug B messaging. Gap-free data delivery meets operational requirements for power generation systems, and successful real-time integration with Databricks is accelerating analytics processing with immediate ROI.
Performance is exceptional, with zero data loss during network outages, sub-second latency, and 99.9% uptime across all 400+ sites connected. HiveMQ is now a mission-critical part of FPL’s digital grid modernization efforts - powering intelligence, resilience, and efficiency across one of the world’s largest energy infrastructures.
