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Gigamon Announces Multi-Purpose Visibility Fabric Node for Big Data Traffic Intelligence

Gigamon announced the GigaVUE-HC2 fabric node for the Services Layer of the Visibility Fabric architecture.

The GigaVUE-HC2 combines the elasticity of compute with the speed and performance needed to address the ever-changing monitoring and security landscape. Security, performance and customer experience monitoring are rapidly transforming due to the increasing adoption of cloud, consumerization of IT, mobility and the evolution of the Internet of Things. These trends are creating the need for sophisticated, stateful and intelligent traffic correlation, as well as the need to accomplish everything at unprecedented speeds.

“The GigaVUE-HC2 provides a versatile multi-purpose platform that addresses the needs of a broad variety of tools. Its performance and GigaSMART intelligence addresses not only the monitoring needs of today, but scales to meet the needs of the future,” said Shehzad Merchant, Chief Strategy Officer at Gigamon. “Big Data traffic intelligence requires compute elasticity to meet the dynamic needs of today’s organizations. Gigamon’s architecture supports distributed intelligence, centralized intelligence, or a hybrid of the two and ensures that multiple H-Series platforms can be managed as a single virtual chassis in a cluster. GigaVUE-HC2 offers traffic intelligence at the right place in the network for the right price.”

The GigaVUE-HC2 is a modular platform that combines multiple external functions such as compute for stateful and intelligent traffic correlation, secure in-band TAPs to prevent unauthorized access to traffic data, and sophisticated filtering, replication and aggregation, all in a single, compact modular form factor thereby eliminating the need for service chaining multiple external nodes.

The GigaVUE-HC2 will launch with seven optional modules including 10Gb and 40Gb modules, GigaSMART intelligence as well as copper and fiber TAPs. The optional GigaSMART capabilities include packet slicing, masking, source port labeling, tunneling, header stripping and Layer 7 load balancing.

Visibility Fabric Applications provide stateful packet correlation capabilities that enable de-duplication, provide session awareness for enhanced visibility, insight and control over data traffic flows, as well as the ability to summarize and generate NetFlow statistics from incoming traffic streams.

“The new GigaVUE-HC2 demonstrates Gigamon’s commitment to provide current and future customers with innovative Visibility Fabric platforms and solutions,” said Bob Laliberte, Sr. Analyst with ESG. “The combination of a port dense 2RU footprint, GigaSMART capabilities and a full suite of applications - that deliver network tapping, traffic aggregation, de-duplication, and NetFlow generation - will enable organizations to maximize functionality and versatility, while minimizing its data center footprint.”

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Gigamon Announces Multi-Purpose Visibility Fabric Node for Big Data Traffic Intelligence

Gigamon announced the GigaVUE-HC2 fabric node for the Services Layer of the Visibility Fabric architecture.

The GigaVUE-HC2 combines the elasticity of compute with the speed and performance needed to address the ever-changing monitoring and security landscape. Security, performance and customer experience monitoring are rapidly transforming due to the increasing adoption of cloud, consumerization of IT, mobility and the evolution of the Internet of Things. These trends are creating the need for sophisticated, stateful and intelligent traffic correlation, as well as the need to accomplish everything at unprecedented speeds.

“The GigaVUE-HC2 provides a versatile multi-purpose platform that addresses the needs of a broad variety of tools. Its performance and GigaSMART intelligence addresses not only the monitoring needs of today, but scales to meet the needs of the future,” said Shehzad Merchant, Chief Strategy Officer at Gigamon. “Big Data traffic intelligence requires compute elasticity to meet the dynamic needs of today’s organizations. Gigamon’s architecture supports distributed intelligence, centralized intelligence, or a hybrid of the two and ensures that multiple H-Series platforms can be managed as a single virtual chassis in a cluster. GigaVUE-HC2 offers traffic intelligence at the right place in the network for the right price.”

The GigaVUE-HC2 is a modular platform that combines multiple external functions such as compute for stateful and intelligent traffic correlation, secure in-band TAPs to prevent unauthorized access to traffic data, and sophisticated filtering, replication and aggregation, all in a single, compact modular form factor thereby eliminating the need for service chaining multiple external nodes.

The GigaVUE-HC2 will launch with seven optional modules including 10Gb and 40Gb modules, GigaSMART intelligence as well as copper and fiber TAPs. The optional GigaSMART capabilities include packet slicing, masking, source port labeling, tunneling, header stripping and Layer 7 load balancing.

Visibility Fabric Applications provide stateful packet correlation capabilities that enable de-duplication, provide session awareness for enhanced visibility, insight and control over data traffic flows, as well as the ability to summarize and generate NetFlow statistics from incoming traffic streams.

“The new GigaVUE-HC2 demonstrates Gigamon’s commitment to provide current and future customers with innovative Visibility Fabric platforms and solutions,” said Bob Laliberte, Sr. Analyst with ESG. “The combination of a port dense 2RU footprint, GigaSMART capabilities and a full suite of applications - that deliver network tapping, traffic aggregation, de-duplication, and NetFlow generation - will enable organizations to maximize functionality and versatility, while minimizing its data center footprint.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...