Ixia has partnered with Dynatrace to deliver enterprise application performance assurance.
This is accomplished through deep and wide network visibility that enables enterprise customers to identify and correct application issues before impacting the user experience.
Dynatrace’s Data Center Real User Monitoring (DC RUM) delivers end-user and transaction-centric performance monitoring for dynamic data centers, providing visibility into software-defined networks and accelerating the transition of organizations to more service-oriented IT operations.
“End-to-end network visibility is critical for the DC RUM solution to monitor and ensure a quality user experience,” said Scott Westlake, Vice President of Business Development at Ixia. “Exploding data volumes and increasing speeds are making it difficult for IT administrators to monitor application performance. Ixia’s Vision ONE™ solves the challenge of monitoring large volumes of traffic by providing DC RUM both packet and flow data.”
Dynatrace’s integration with Ixia’s NetFlow data feed, IxFlow, provides DC RUM content and context rich flow data, for a wide view of the network. By analyzing performance metrics in the context of the user’s experience, DC RUM enables IT teams to prioritize and solve problems directly impacting their users, helping organizations save valuable time and resources.
Ixia’s application level filtering also enhances deep dive monitoring and troubleshooting by removing unnecessary traffic, or data packets. This supports DC RUM to not only keep pace with increasing network data volumes, but speed application performance monitoring to ensure rapid identification of application issues before impacting the user experience.
“Dynatrace customers rely on our solutions to deliver powerful insights and analytics,” said Steve Tack, Senior Vice President at Dynatrace. “Many of our customers rely on Ixia technology to provide deep dive packet level visibility into business critical applications like SAP, Citrix and Oracle, as well as wide flow visibility to help facilitate performance analysis and troubleshooting. Our integration with Ixia’s IxFlow provides our mutual customers scalable end to end visibility.”
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.