Skip to main content

Splunk Introduces Splunk App for Stream

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Splunk Introduces Splunk App for Stream

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...