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Splunk Enterprise Version 6.2 Released

Splunk announced the general availability (GA) of Splunk Enterprise 6.2, the latest version of the award-winning platform for machine data, and version 6.2 of Hunk: Splunk Analytics for Hadoop and NoSQL Data Stores.

Splunk Enterprise 6.2 delivers simplified analysis and powerful pattern detection that enables more users across IT and the business to discover relationships in their data and build advanced analytics. Hunk is now also available directly from the Amazon Elastic MapReduce (Amazon EMR) console and priced on an hourly basis.

“The latest versions of Splunk Enterprise and Hunk significantly advance the capability to deliver powerful analytics to a broad range of new users,” said Guido Schroeder, SVP of Products, Splunk. “Splunk Enterprise 6.2 also reduces total cost of ownership through improved scalability; and Hunk 6.2 on AWS EMR drastically decreases time to value for anyone looking to gain value out of data they have been storing in Hadoop.”

Splunk Enterprise 6.2 puts powerful analytics in the hands of even more users. New features in Splunk Enterprise 6.2 include:

- Easier Data Onboarding: New intuitive wizard makes it easier to onboard any machine data. New interface guides users through previewing, onboarding and preparation of machine data for downstream analysis.

- Instant Pivot: Pivot directly on any machine data, enabling powerful analysis and rapid creation of dashboards without advanced knowledge of Splunk Search Processing Language.

- Enhanced Event Pattern Detection: Speeds analysis by automatically grouping similar events to discover meaningful patterns in the underlying machine data.

- Search Head Clustering: Reduce total cost of ownership by increasing concurrent user capacity and eliminating shared storage requirements.

Hunk 6.2 extends the power of exploratory analytics and enables all professionals to easily unlock the business value of data in Hadoop and NoSQL data stores. New features in Hunk 6.2 include:

- Amazon EMR Console 1-Click Purchase: For the first time ever, leverage automatically configured Hunk instances provisioned by AWS, priced hourly, for data in Amazon EMR.

- Hunk Sandbox: Rapidly learn Hunk interactive search and analytics in a single download that runs on the leading operating systems, without having to set up a Hadoop cluster.

- Hunk Apps: Search, analyze and visualize data in NoSQL and other data stores through prepackaged connections, including the Hunk App for MongoDB and Sqrrl App for Hunk (Apache Accumulo). Gain insight into the health of your AWS Elastic Load Balancing services with the Hunk App for AWS Elastic Load Balancing.

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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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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 ...

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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 ...

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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 Enterprise Version 6.2 Released

Splunk announced the general availability (GA) of Splunk Enterprise 6.2, the latest version of the award-winning platform for machine data, and version 6.2 of Hunk: Splunk Analytics for Hadoop and NoSQL Data Stores.

Splunk Enterprise 6.2 delivers simplified analysis and powerful pattern detection that enables more users across IT and the business to discover relationships in their data and build advanced analytics. Hunk is now also available directly from the Amazon Elastic MapReduce (Amazon EMR) console and priced on an hourly basis.

“The latest versions of Splunk Enterprise and Hunk significantly advance the capability to deliver powerful analytics to a broad range of new users,” said Guido Schroeder, SVP of Products, Splunk. “Splunk Enterprise 6.2 also reduces total cost of ownership through improved scalability; and Hunk 6.2 on AWS EMR drastically decreases time to value for anyone looking to gain value out of data they have been storing in Hadoop.”

Splunk Enterprise 6.2 puts powerful analytics in the hands of even more users. New features in Splunk Enterprise 6.2 include:

- Easier Data Onboarding: New intuitive wizard makes it easier to onboard any machine data. New interface guides users through previewing, onboarding and preparation of machine data for downstream analysis.

- Instant Pivot: Pivot directly on any machine data, enabling powerful analysis and rapid creation of dashboards without advanced knowledge of Splunk Search Processing Language.

- Enhanced Event Pattern Detection: Speeds analysis by automatically grouping similar events to discover meaningful patterns in the underlying machine data.

- Search Head Clustering: Reduce total cost of ownership by increasing concurrent user capacity and eliminating shared storage requirements.

Hunk 6.2 extends the power of exploratory analytics and enables all professionals to easily unlock the business value of data in Hadoop and NoSQL data stores. New features in Hunk 6.2 include:

- Amazon EMR Console 1-Click Purchase: For the first time ever, leverage automatically configured Hunk instances provisioned by AWS, priced hourly, for data in Amazon EMR.

- Hunk Sandbox: Rapidly learn Hunk interactive search and analytics in a single download that runs on the leading operating systems, without having to set up a Hadoop cluster.

- Hunk Apps: Search, analyze and visualize data in NoSQL and other data stores through prepackaged connections, including the Hunk App for MongoDB and Sqrrl App for Hunk (Apache Accumulo). Gain insight into the health of your AWS Elastic Load Balancing services with the Hunk App for AWS Elastic Load Balancing.

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 ...