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Splunk Launches Splunk Enterprise 5

Splunk announced the general availability of Splunk Enterprise 5, the fastest, most resilient version of the company's flagship product.

The latest release includes added features to create a powerful platform for developers building big data applications.

"Even as data volume and complexity are growing exponentially, the time people are willing to wait for answers is shrinking," said Guido Schroeder, senior vice president of products, Splunk.

"Technology needs to provide answers as quickly as users think of questions, regardless of the speed, complexity and scale of the underlying data. Users need to be able to use their data in ways that help to achieve operational intelligence. We need to put that technology in the hands of developers and IT professionals so they can innovate and drive new ideas. It is for these reasons and more that we created Splunk Enterprise 5."

"With the added pressure on IT professionals to meet performance goals and rapidly introduce new services and control costs, buyers have shifted towards technologies that provide real-time operational visibility and analytics across their mission-critical infrastructures," said Jonah Kowall, research director, IT operations management, Gartner. "These buyers are looking to take advantage of technologies which rely on commodity servers with automatic data redundancy and grid computing models."

Reports are up to 1,000 times faster and dashboards are easier to navigate and share with Splunk Enterprise 5. Dynamic drilldowns integrate simple workflows, providing a more intuitive user experience. Integrated PDFs enable reports or dashboards to be shared with anyone on demand or on a scheduled basis.

Splunk Enterprise 5 introduces patent pending Index Replication that delivers built-in high availability and enterprise-class resilience, while scaling on commodity servers and storage. As data is collected and indexed, multiple identical copies are maintained. During an outage, incoming data continues to get indexed and indexed data continues to be searchable. Set up is simple and management is done through the Splunk Manager user interface.

Splunk Enterprise 5 also contains significant platform features to drive greater extensibility, modularity and interoperability.

For developers, this release includes a robust, versioned API and JavaScript SDK. SDKs are also available for Java, Python and PHP. Each SDK includes comprehensive documentation, resources and tools to help developers accelerate application development and testing. This enables developers to better leverage the Splunk platform by integrating it into their IT infrastructure and to provide a familiar development environment for building big data applications.

Splunk Enterprise 5 also includes features that enable users to capitalize on the full power of Splunk-Hadoop integration. Splunk Hadoop Connect provides bi-directional integration to easily and reliably move data between Splunk Enterprise and Hadoop. This makes it easier to stand up reliable, secure, enterprise-grade big data projects in days instead of months.

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 ...

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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 ...

Splunk Launches Splunk Enterprise 5

Splunk announced the general availability of Splunk Enterprise 5, the fastest, most resilient version of the company's flagship product.

The latest release includes added features to create a powerful platform for developers building big data applications.

"Even as data volume and complexity are growing exponentially, the time people are willing to wait for answers is shrinking," said Guido Schroeder, senior vice president of products, Splunk.

"Technology needs to provide answers as quickly as users think of questions, regardless of the speed, complexity and scale of the underlying data. Users need to be able to use their data in ways that help to achieve operational intelligence. We need to put that technology in the hands of developers and IT professionals so they can innovate and drive new ideas. It is for these reasons and more that we created Splunk Enterprise 5."

"With the added pressure on IT professionals to meet performance goals and rapidly introduce new services and control costs, buyers have shifted towards technologies that provide real-time operational visibility and analytics across their mission-critical infrastructures," said Jonah Kowall, research director, IT operations management, Gartner. "These buyers are looking to take advantage of technologies which rely on commodity servers with automatic data redundancy and grid computing models."

Reports are up to 1,000 times faster and dashboards are easier to navigate and share with Splunk Enterprise 5. Dynamic drilldowns integrate simple workflows, providing a more intuitive user experience. Integrated PDFs enable reports or dashboards to be shared with anyone on demand or on a scheduled basis.

Splunk Enterprise 5 introduces patent pending Index Replication that delivers built-in high availability and enterprise-class resilience, while scaling on commodity servers and storage. As data is collected and indexed, multiple identical copies are maintained. During an outage, incoming data continues to get indexed and indexed data continues to be searchable. Set up is simple and management is done through the Splunk Manager user interface.

Splunk Enterprise 5 also contains significant platform features to drive greater extensibility, modularity and interoperability.

For developers, this release includes a robust, versioned API and JavaScript SDK. SDKs are also available for Java, Python and PHP. Each SDK includes comprehensive documentation, resources and tools to help developers accelerate application development and testing. This enables developers to better leverage the Splunk platform by integrating it into their IT infrastructure and to provide a familiar development environment for building big data applications.

Splunk Enterprise 5 also includes features that enable users to capitalize on the full power of Splunk-Hadoop integration. Splunk Hadoop Connect provides bi-directional integration to easily and reliably move data between Splunk Enterprise and Hadoop. This makes it easier to stand up reliable, secure, enterprise-grade big data projects in days instead of months.

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 ...