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Splunk Introduces Splunk Enterprise 6.2

Splunk announced Splunk Enterprise 6.2, the latest version of the platform for machine data.

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. The new release also reduces total cost of ownership by improving scalability of concurrent searches and eliminating shared storage requirements.

Splunk Enterprise 6.2 will be generally available as software for on premises, cloud or hybrid deployments, and as a cloud service through Splunk Cloud on Tuesday, October 28.

“Splunk Enterprise 6.2 gives easier, more intuitive analysis to casual and less technical users, through enhanced automated discovery of valuable patterns in the machine data. It is the latest advancement in our focus to deliver stronger IT and business insights through powerful, yet easy-to-use, analytics that can be created and used across the organization,” said Guido Schroeder, SVP of Products, Splunk. “With improved scalability, elimination of shared storage requirements, and a new Distributed Management Console, Splunk Enterprise 6.2 will also drive greater efficiency for the thousands of organizations that rely on Splunk to gain operational intelligence.”

“As the complexity of deploying and managing IT infrastructures continues to intensify, there is a growing demand for analytics platforms that enhance visibility and extend insights,” said Tim Grieser, Program VP, Enterprise System Management Software, IDC. “The latest Splunk Enterprise release, featuring analytics creation for non-specialist users and unique pattern detection capabilities, builds on past successes to focus on these requirements.”

Key features in Splunk Enterprise 6.2 include:

■ Easier Data Onboarding and Preparation

- New intuitive wizard makes it easier to onboard any machine data. New interfaces guide users through previewing, onboarding and preparation of machine data for downstream analysis.

- Advanced Field Extractor delivers simplified identification, naming and tagging of fields in machine data for rapid analysis.

■ More Powerful Analytics for Everyone

- Instant Pivot allows anyone to pivot directly from any search, enabling powerful analysis and rapid creation of dashboards without knowledge of Splunk Search Processing Language.

- Enhanced event Pattern Detection speeds analysis by automatically discovering meaningful patterns in underlying machine data.

- Prebuilt Panels enable faster dashboard creation by providing the ability to create, package and share reusable dashboard building blocks.

■ Simplified Management at Scale

- Search head clustering advancement reduces total cost of ownership by increasing concurrent user capacity and eliminating shared storage requirements. Also improves redundancy and replication of search results.

- Distributed Management Console delivers a new interface to centrally monitor the health and performance of distributed Splunk Enterprise deployments.

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Splunk Introduces Splunk Enterprise 6.2

Splunk announced Splunk Enterprise 6.2, the latest version of the platform for machine data.

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. The new release also reduces total cost of ownership by improving scalability of concurrent searches and eliminating shared storage requirements.

Splunk Enterprise 6.2 will be generally available as software for on premises, cloud or hybrid deployments, and as a cloud service through Splunk Cloud on Tuesday, October 28.

“Splunk Enterprise 6.2 gives easier, more intuitive analysis to casual and less technical users, through enhanced automated discovery of valuable patterns in the machine data. It is the latest advancement in our focus to deliver stronger IT and business insights through powerful, yet easy-to-use, analytics that can be created and used across the organization,” said Guido Schroeder, SVP of Products, Splunk. “With improved scalability, elimination of shared storage requirements, and a new Distributed Management Console, Splunk Enterprise 6.2 will also drive greater efficiency for the thousands of organizations that rely on Splunk to gain operational intelligence.”

“As the complexity of deploying and managing IT infrastructures continues to intensify, there is a growing demand for analytics platforms that enhance visibility and extend insights,” said Tim Grieser, Program VP, Enterprise System Management Software, IDC. “The latest Splunk Enterprise release, featuring analytics creation for non-specialist users and unique pattern detection capabilities, builds on past successes to focus on these requirements.”

Key features in Splunk Enterprise 6.2 include:

■ Easier Data Onboarding and Preparation

- New intuitive wizard makes it easier to onboard any machine data. New interfaces guide users through previewing, onboarding and preparation of machine data for downstream analysis.

- Advanced Field Extractor delivers simplified identification, naming and tagging of fields in machine data for rapid analysis.

■ More Powerful Analytics for Everyone

- Instant Pivot allows anyone to pivot directly from any search, enabling powerful analysis and rapid creation of dashboards without knowledge of Splunk Search Processing Language.

- Enhanced event Pattern Detection speeds analysis by automatically discovering meaningful patterns in underlying machine data.

- Prebuilt Panels enable faster dashboard creation by providing the ability to create, package and share reusable dashboard building blocks.

■ Simplified Management at Scale

- Search head clustering advancement reduces total cost of ownership by increasing concurrent user capacity and eliminating shared storage requirements. Also improves redundancy and replication of search results.

- Distributed Management Console delivers a new interface to centrally monitor the health and performance of distributed Splunk Enterprise deployments.

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 ...