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FogHorn Releases Lightning Edge Intelligence Software for IoT

FogHorn Systems announced the general availability of its new Lightning software platform for real-time analytics applications running on ultra-small footprint edge devices.

Lightning allows application developers, systems integrators and production engineers to quickly and easily build high-performance edge analytics solutions for their industrial operations and Industrial IoT (IIoT) use cases, and rapidly deploy those applications throughout highly-dispersed distributed edge environments. By reducing bandwidth usage and costs, minimizing latency, and increasing reliability, FogHorn enables real-time responsiveness that is critical to a growing number of IIoT applications.

“FogHorn is revolutionizing the development of high-value IoT application solutions in a huge variety of industrial and commercial settings by bringing the power of ‘big data’ intelligence to the source of high-volume and high-velocity machine data at the edge, rather than transporting that data to the cloud or data center for upstream processing,” said FogHorn CEO David C. King. “Lightning enables our end customers and their technology partners to build a powerful new class of real-time edge analytics IIoT solutions by minimizing application latency, as well as saving those customers an enormous amount of money associated with bandwidth and cloud hosting costs. Our initial successes have been with major players in the manufacturing, energy, transportation and smart cities sectors.”

FogHorn’s Lightning software platform allows businesses with distributed operations to derive actionable insights as close as possible to geographically dispersed IoT-connected machines and the operations technology (OT) control systems and sensors attached to those machines. Using Lightning, businesses can accelerate their digital transformation projects by spending less time and money on bandwidth costs and end-to-end integration tasks, and focusing more on the delivery of next-generation applications to optimize machine performance, increase total output, improve process yield, and reduce both production and energy consumption costs.

According to John L. Myers, managing Research Director at Enterprise Management Associates (EMA), the growth of connected IoT devices and sensors is driving an increase in new and disruptive business models across industries. “Data processing at the edge is disruptive because it enables industrial companies to tap into operational data for making decisions in real-time and at significant scale,” Myers said. “Using data from IoT sensors to drive immediate action was not possible when data was processed in the cloud and not at the network edge. The benefits of edge computing solutions such as FogHorn’s could extend well beyond cost savings and factory yield optimization, to intelligent management and forecasting.”

“At FogHorn, we solved the biggest challenges associated with gaining data insights at the edge, such as processing and correlating massive amounts of sensor data in real-time,” said FogHorn CTO Sastry Malladi. “The high bandwidth costs of sending data from thousands of devices in remote deployment locations to the cloud for later processing is eliminated or significantly reduced. Bringing powerful analytics closer to the data source is made possible through our patent-pending, high-performance, small-footprint edge analytics engine and other key technology innovations we have introduced at the data ingestion, data processing and data publication layers of the Lightning edge software stack.”

FogHorn Lightning is now available directly from FogHorn as well as a growing ecosystem of Lightning-certified IIoT application developer partners. Lightning is also accessible on the Microsoft Azure Marketplace and FogHorn is a certified SAP HANA application solution partner. In terms of IoT gateway hardware support, FogHorn is a certified Dell IoT solution partner and Lightning has also been validated on HPE Edgeline IoT Gateways as well as other Intel x86 IoT server platforms.

Available immediately, FogHorn’s Lightning edge intelligence software platform is currently available in two different versions.

Lightning Micro Edition is embeddable software with a very small memory footprint (less than 256 MB) required for data processing and real-time analytics at the edge.

Features include:

- High-speed data ingestion via OPC-UA, MQTT, Modbus and other protocols

- Data transformation and enrichment

- VEL, a real-time streaming analytic engine with an easy-to-use expression language and hundreds of built-in functions

- Low footprint Edge Application development using FogHorn's C++ SDK

Lightning Standard Edition includes all of the features of Lightning Micro edition with additional support for advanced analytics, and edge applications in different languages.

These additional features include:

- Built-in time series database for historical analysis

- Dashboard visualizer for real-time insights and a machine learning sandbox with commonly used algorithms

- Edge Application Development SDK in multiple languages (Java, Python, C++)

- Data publication to external/cloud based data stores such as Apache Hadoop, Kafka, Microsoft Azure, Cloud Foundry RIAK, etc.

The software installation can be managed remotely from a central management console for both these versions of the software with the following capabilities.

- Management console scaling to thousands of edge deployments

- Ability for users to add, manage, and monitor all connected devices and sensors from a single pane of glass

- Application deployment and monitoring

- VEL Analytic expression authoring and deployment

- Simplified edge configurations for easily repeatable deployments.

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FogHorn Releases Lightning Edge Intelligence Software for IoT

FogHorn Systems announced the general availability of its new Lightning software platform for real-time analytics applications running on ultra-small footprint edge devices.

Lightning allows application developers, systems integrators and production engineers to quickly and easily build high-performance edge analytics solutions for their industrial operations and Industrial IoT (IIoT) use cases, and rapidly deploy those applications throughout highly-dispersed distributed edge environments. By reducing bandwidth usage and costs, minimizing latency, and increasing reliability, FogHorn enables real-time responsiveness that is critical to a growing number of IIoT applications.

“FogHorn is revolutionizing the development of high-value IoT application solutions in a huge variety of industrial and commercial settings by bringing the power of ‘big data’ intelligence to the source of high-volume and high-velocity machine data at the edge, rather than transporting that data to the cloud or data center for upstream processing,” said FogHorn CEO David C. King. “Lightning enables our end customers and their technology partners to build a powerful new class of real-time edge analytics IIoT solutions by minimizing application latency, as well as saving those customers an enormous amount of money associated with bandwidth and cloud hosting costs. Our initial successes have been with major players in the manufacturing, energy, transportation and smart cities sectors.”

FogHorn’s Lightning software platform allows businesses with distributed operations to derive actionable insights as close as possible to geographically dispersed IoT-connected machines and the operations technology (OT) control systems and sensors attached to those machines. Using Lightning, businesses can accelerate their digital transformation projects by spending less time and money on bandwidth costs and end-to-end integration tasks, and focusing more on the delivery of next-generation applications to optimize machine performance, increase total output, improve process yield, and reduce both production and energy consumption costs.

According to John L. Myers, managing Research Director at Enterprise Management Associates (EMA), the growth of connected IoT devices and sensors is driving an increase in new and disruptive business models across industries. “Data processing at the edge is disruptive because it enables industrial companies to tap into operational data for making decisions in real-time and at significant scale,” Myers said. “Using data from IoT sensors to drive immediate action was not possible when data was processed in the cloud and not at the network edge. The benefits of edge computing solutions such as FogHorn’s could extend well beyond cost savings and factory yield optimization, to intelligent management and forecasting.”

“At FogHorn, we solved the biggest challenges associated with gaining data insights at the edge, such as processing and correlating massive amounts of sensor data in real-time,” said FogHorn CTO Sastry Malladi. “The high bandwidth costs of sending data from thousands of devices in remote deployment locations to the cloud for later processing is eliminated or significantly reduced. Bringing powerful analytics closer to the data source is made possible through our patent-pending, high-performance, small-footprint edge analytics engine and other key technology innovations we have introduced at the data ingestion, data processing and data publication layers of the Lightning edge software stack.”

FogHorn Lightning is now available directly from FogHorn as well as a growing ecosystem of Lightning-certified IIoT application developer partners. Lightning is also accessible on the Microsoft Azure Marketplace and FogHorn is a certified SAP HANA application solution partner. In terms of IoT gateway hardware support, FogHorn is a certified Dell IoT solution partner and Lightning has also been validated on HPE Edgeline IoT Gateways as well as other Intel x86 IoT server platforms.

Available immediately, FogHorn’s Lightning edge intelligence software platform is currently available in two different versions.

Lightning Micro Edition is embeddable software with a very small memory footprint (less than 256 MB) required for data processing and real-time analytics at the edge.

Features include:

- High-speed data ingestion via OPC-UA, MQTT, Modbus and other protocols

- Data transformation and enrichment

- VEL, a real-time streaming analytic engine with an easy-to-use expression language and hundreds of built-in functions

- Low footprint Edge Application development using FogHorn's C++ SDK

Lightning Standard Edition includes all of the features of Lightning Micro edition with additional support for advanced analytics, and edge applications in different languages.

These additional features include:

- Built-in time series database for historical analysis

- Dashboard visualizer for real-time insights and a machine learning sandbox with commonly used algorithms

- Edge Application Development SDK in multiple languages (Java, Python, C++)

- Data publication to external/cloud based data stores such as Apache Hadoop, Kafka, Microsoft Azure, Cloud Foundry RIAK, etc.

The software installation can be managed remotely from a central management console for both these versions of the software with the following capabilities.

- Management console scaling to thousands of edge deployments

- Ability for users to add, manage, and monitor all connected devices and sensors from a single pane of glass

- Application deployment and monitoring

- VEL Analytic expression authoring and deployment

- Simplified edge configurations for easily repeatable deployments.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.