Skip to main content

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 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...