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ExtraHop and Splunk Partner on APM

ExtraHop Networks has collaborated with Splunk to offer end-to-end visibility of application environments.

The Splunk App for ExtraHop helps to visualize and analyze ExtraHop data in Splunk.

The policy-based logging capability in the ExtraHop system enables IT Operations teams to achieve the following goals:

- Generate consistently formatted logs across heterogeneous datacenter components, such as network storage systems. The ExtraHop system provides logging of networking, application, database, and storage metrics in a consistent manner regardless of vendor.

- Log high-priority events, anomalies, or thresholds with precision and according to set policies.

- Log specific correlated network, web, VDI, database, or storage events monitored by the ExtraHop system, such as when database transactions exceed a set amount from one or more specified clients or file access times across heterogeneous networked storage systems exceed a certain threshold.

"We are excited about the ExtraHop and Splunk integration,” said Jesse Rothstein, CEO, ExtraHop Networks. “Our policy-based logging capability enables customers to export metrics to Splunk Enterprise that they otherwise could not because of performance concerns — such as end-user experience, network performance trends, database queries, and storage internals. Several forward-thinking customers are already using ExtraHop and Splunk together to meet their complete application performance management needs. The integration of our products will accelerate our customers’ time-to-value and enable them to retire their legacy APM tools.”

“Both Splunk and ExtraHop are pushing the envelope for application performance management,” said Bill Gaylord, SVP of Business Development, Splunk. “With the correlation that Splunk Enterprise provides between ExtraHop data and other IT data sources, datacenter operations teams have a whole new level of visibility and control.”

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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 ...

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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 ...

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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 ...

ExtraHop and Splunk Partner on APM

ExtraHop Networks has collaborated with Splunk to offer end-to-end visibility of application environments.

The Splunk App for ExtraHop helps to visualize and analyze ExtraHop data in Splunk.

The policy-based logging capability in the ExtraHop system enables IT Operations teams to achieve the following goals:

- Generate consistently formatted logs across heterogeneous datacenter components, such as network storage systems. The ExtraHop system provides logging of networking, application, database, and storage metrics in a consistent manner regardless of vendor.

- Log high-priority events, anomalies, or thresholds with precision and according to set policies.

- Log specific correlated network, web, VDI, database, or storage events monitored by the ExtraHop system, such as when database transactions exceed a set amount from one or more specified clients or file access times across heterogeneous networked storage systems exceed a certain threshold.

"We are excited about the ExtraHop and Splunk integration,” said Jesse Rothstein, CEO, ExtraHop Networks. “Our policy-based logging capability enables customers to export metrics to Splunk Enterprise that they otherwise could not because of performance concerns — such as end-user experience, network performance trends, database queries, and storage internals. Several forward-thinking customers are already using ExtraHop and Splunk together to meet their complete application performance management needs. The integration of our products will accelerate our customers’ time-to-value and enable them to retire their legacy APM tools.”

“Both Splunk and ExtraHop are pushing the envelope for application performance management,” said Bill Gaylord, SVP of Business Development, Splunk. “With the correlation that Splunk Enterprise provides between ExtraHop data and other IT data sources, datacenter operations teams have a whole new level of visibility and control.”

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 ...