Splunk announced the general availability of the Splunk App for Stream. This app offers a new software approach for capturing real-time streaming wire data. Wire data is a unique kind of machine data transmitted between applications over networks, which can provide information about business activity, application performance, security and IT infrastructure issues, without needing code instrumentation. The Splunk App for Stream easily captures wire data for additional insights when using Splunk Enterprise and Splunk Cloud for security, fraud detection, compliance, application management, IT operations and business analytics.
The Splunk App for Stream is free for Splunk Enterprise or Splunk Cloud customers.
“The Splunk App for Stream, the first product delivered from our acquisition of Cloudmeter last year, is a new approach that further enhances the value that customers can realize with Splunk software,” said Leena Joshi, senior director of solutions marketing, Splunk. “Unlike traditional and appliance-based solutions, which are difficult to deploy, especially in public cloud infrastructures, the Splunk App for Stream enables customers to gain immediate wire data access on-premises or in public, private or hybrid cloud infrastructures. It opens up for our customers a whole new class of data sets to provide continuous IT, security and business insights.”
“The Splunk App for Stream is a valuable new extension to the Splunk software platform,” said Peter Christy, research director at 451 Research. “The in-depth analysis of wire data given Splunk’s proven scalability and powerful analytics could be a game-changer for the IT industry.”
The Splunk App for Stream can be rapidly deployed to collect, aggregate and filter wire data from both network endpoints, such as virtual machines in public clouds or virtual desktops, and the network perimeter, such as routers, switches and firewalls. With fine-grained filters and aggregation rules defined through the app interface, customers can dynamically control data volumes and capture only the wire data that is relevant for the needs of their specific analysis. By correlating wire data with other machine data, such as logs, events and metrics, customers can now gain new valuable insights into application and infrastructure performance, operational issues, transaction paths, system downtime, infrastructure relationships, security vulnerabilities, compliance and customer behavior.
Top use cases for the Splunk App for Stream include:
- Application Management: Provides granular data on transaction response times, transaction traces, transaction paths, network performance and database queries without requiring any instrumentation of the application.
- IT Operations: Empowers administrators to pinpoint root-cause of issues faster, map dependencies of critical infrastructure services and ensure the delivery of services at the levels required by the business.
- Security: Enables in-depth monitoring and real-time correlation to drive sophisticated analytics on breaches, threat detection, intelligence gathering and threat prevention. Can be deployed in the midst of a breach/incident investigation to gain insight into network traffic from any system of interest not previously monitored.
- Business Analytics: Captures web interactions and key metrics such as time spent on page, bounce rates, navigation paths and product performance, without the need to tag individual pages. Improves customer satisfaction and conversions, prevents drop-offs and can boost online revenues. Enables real time end-to-end insights into business processes such as order management, provisioning, trade execution span and others, without requiring specific instrumentation.
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 ...