
Corvil announced its Tera Release, the latest version of the Corvil network data analytics platform.
The new platform democratizes the power of network data, with an all-new, intuitive and customizable user interface and a new data automation engine that dramatically reduces the time, expense, and complexity of working with network data.
In addition, the Tera Release adds a new portfolio of real-time security analytics, thus giving Network Operations, Application Operations and Security Operations teams an accurate and collaborative real-time picture of critical service chains across their business.
"We believe that the most effective way for IT to assure and safeguard the delivery of critical applications, services, and data to the business is for all IT teams involved to have a common, trusted, granular source of shared data," said Donal Byrne, CEO, Corvil. "Network data is widely regarded as the most granular and powerful source of real-time data that can be used for this purpose. The challenge is to make network data analytics super-easy, cost-effective and widely available to all. We believe that our new Tera Release achieves this objective with our customers reporting up to 90 percent reduction in time for IT Ops to see, analyze and act on critical business application flows at a cost that is less than what the network team traditionally spends on legacy network probes."
Key innovations in the Corvil Tera Release include:
MULTI TEAM USER INTERFACE - The Tera Release re-imagines the Corvil user experience by providing a new HTML5-based user interface with polished, intuitive, and customizable dashboards that have been optimized to perform workflows for network, application, and security operations professionals jointly responsible for delivery of critical business.
SELF POPULATING DASHBOARDS - The Tera data engine automatically discovers application and business data flows within raw network data with zero configuration. The data in these flows is decoded, transformed, and self-populated into tables and graphical widgets, giving the full picture for what is happening across a business in real-time.
REAL-TIME SECURITY OPERATIONS INTEGRATION - Network data has traditionally been used for network forensics by the security operations team. New thinking in this area suggests that security operations should be leveraging the valuable information contained in network performance monitoring and diagnostic tools. Gartner recently commented: "Network performance monitoring tool data provided by IT operations to security operations for analysis of network forensic information can play a key role in solving security incidents." The new Tera release delivers on this new thinking and goes further by seamlessly integrating live threat intelligence and real-time network forensics with leading SIEM platforms. For example, the Tera release consumes threat intelligence from iSIGHT Partners, and identifies related suspicious activity from streaming analysis of network data. It then forwards these security events into a SIEM platform, like Splunk, using Corvil Streams. The event stream contains associated metadata relating to the threat intel, in addition to a link that allows click-back to Corvil for further retrospective analysis of the security incident.
PROGRAMMABLE STREAM AND/OR STORE NETWORK DATA LAKE - Unlike other platforms in the industry that either capture and store network data before analysis or analyze network data on the fly and then discard the decoded data, the Tera Release is fully user programmable so that customers can decide for themselves how much data to keep, and for how long. The streaming data analytics architecture used by Corvil analyzes all network data on the fly and then programmatically stores both raw network data and enriched network data. The resulting time-synchronized, distributed data store is automatically maintained and managed by the Corvil engines, allowing the user complete flexibility in the creation and management of their network data lake. In addition, the Tera Release now supports a broader array of connectors for streaming Corvil data to big data platforms e.g. Cloudera Enterprise Data Hub.
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.