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Corvil Introduces Streaming Analytics Platform

Corvil announced its new streaming analytics platform for real-time operational intelligence and big data integration. The new Corvil Giga release is designed for the Financial Markets industry and others that need to operate their business in true real-time.

Addressing the need to prepare for the new wave of financial regulations, mitigating risk against IT failure and malicious attack, accessing real-time operational intelligence to see, understand and act faster, and managing massive volumes of high-velocity streaming data without busting IT budgets, are all compelling reasons for this new release.

“Our customers want to run their business in the now. This is not simply about real-time data analytics. It is about providing the data and intelligence that delivers a 10x improvement in the speed of operations,” said Donal Byrne, CEO of Corvil. “Over the past several years we have been working with the leading companies within the global financial markets. With massive volumes of data moving at break-neck speed, billions of transactions per day, and ultra sensitivity to IT mishap, the ability to act in the now is second to none. What we’ve done today is to dramatically reduce the barrier to operating and safeguarding their business in real-time.”

The Corvil streaming analytics platform taps into the data that is flowing through the network, and other complementary sources of IT data, transforming it into operational intelligence that can be easily consumed and leveraged to operate business in real-time. Traditionally, accessing these insights has been difficult, error-prone and uneconomic due to the sheer volume and complexity of analyzing data in the network. The Corvil Giga release introduces the most complete set of cost effective, high-performance streaming analytics capabilities on the market.

The new platform introduces the following new capabilities:

- Auto data discovery – automatically discover all data sessions on the network

- Analytics plug-ins – select from hundreds of plug-ins to analyze apps and IT protocols

- Global data search – search all indexed fields to produce instant answers

- Analytics streams – publish analytics streams to big data and BI systems

- Big data adapters – includes support for Hadoop, Kdb, Storm and Splunk

Corvil Giga cuts through the complexities of dealing with network data, and presents meaningful results in real-time that are easily understood by IT operations and business professionals.

“Analytics based on network data can be an immensely rich source of true, real-time insights into business activity. But most organizations have not used it due to challenges of complexity, scale, and cost effectiveness,” said Jim Frey, Vice President, Enterprise Management Associates. “The Corvil solution addresses those challenges by combining streaming analytics with an existing network and application performance monitoring platform that has proved worthy and capable in some of the world’s most demanding, high performance, low latency, and data-intensive environments.”

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Corvil Introduces Streaming Analytics Platform

Corvil announced its new streaming analytics platform for real-time operational intelligence and big data integration. The new Corvil Giga release is designed for the Financial Markets industry and others that need to operate their business in true real-time.

Addressing the need to prepare for the new wave of financial regulations, mitigating risk against IT failure and malicious attack, accessing real-time operational intelligence to see, understand and act faster, and managing massive volumes of high-velocity streaming data without busting IT budgets, are all compelling reasons for this new release.

“Our customers want to run their business in the now. This is not simply about real-time data analytics. It is about providing the data and intelligence that delivers a 10x improvement in the speed of operations,” said Donal Byrne, CEO of Corvil. “Over the past several years we have been working with the leading companies within the global financial markets. With massive volumes of data moving at break-neck speed, billions of transactions per day, and ultra sensitivity to IT mishap, the ability to act in the now is second to none. What we’ve done today is to dramatically reduce the barrier to operating and safeguarding their business in real-time.”

The Corvil streaming analytics platform taps into the data that is flowing through the network, and other complementary sources of IT data, transforming it into operational intelligence that can be easily consumed and leveraged to operate business in real-time. Traditionally, accessing these insights has been difficult, error-prone and uneconomic due to the sheer volume and complexity of analyzing data in the network. The Corvil Giga release introduces the most complete set of cost effective, high-performance streaming analytics capabilities on the market.

The new platform introduces the following new capabilities:

- Auto data discovery – automatically discover all data sessions on the network

- Analytics plug-ins – select from hundreds of plug-ins to analyze apps and IT protocols

- Global data search – search all indexed fields to produce instant answers

- Analytics streams – publish analytics streams to big data and BI systems

- Big data adapters – includes support for Hadoop, Kdb, Storm and Splunk

Corvil Giga cuts through the complexities of dealing with network data, and presents meaningful results in real-time that are easily understood by IT operations and business professionals.

“Analytics based on network data can be an immensely rich source of true, real-time insights into business activity. But most organizations have not used it due to challenges of complexity, scale, and cost effectiveness,” said Jim Frey, Vice President, Enterprise Management Associates. “The Corvil solution addresses those challenges by combining streaming analytics with an existing network and application performance monitoring platform that has proved worthy and capable in some of the world’s most demanding, high performance, low latency, and data-intensive environments.”

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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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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 ...