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Version 3.1 of Splunk App for VMware Released

Splunk announced the general availability of version 3.1 of the Splunk App for VMware, which provides comprehensive operational visibility into virtualized environments.

This release delivers significant new features, including 200 out-of-the-box reports, the ability to instantly identify outliers for real-time triage and built-in correlation into storage systems including a direct drill down into data from NetApp Data ONTAP.

Features of version 3.1 of the Splunk App for VMware include:

- Operational Performance Insights: Visualize the operational health of the VMware environment with more than 200 out-of-the-box, customizable reports; identify underperforming/distressed hosts, virtual machines (VMs) and data stores in real time.

- Advanced Mapping Visualizations: Access interactive, visual maps of your virtual environment, highlighting problems and statistical comparisons based on predefined, customizable thresholds. Immediately identify outliers that fall outside the normal distribution on a statistical map of your VMware environment.

- NetApp and Storage Correlations: Accelerate troubleshooting, optimize capacity and streamline workloads with out-of-the-box correlation between VMware virtualization layer and storage systems, including direct drill down into data from NetApp Data ONTAP storage for in-depth investigation.

- End-to-End Visibility: Correlate data from virtual infrastructure with data from applications, operating systems, hardware and networks to gain operational intelligence across all technology tiers.

- Advanced Analytics: Forecast future resource CPU, memory and disk requirements on VMware VCenter, ESXi hosts and VMs using various predictive algorithms; gain visibility into potential security breaches and non-compliant usage patterns; explore unique errors and exceptions; track changes with visibility into vCenter tasks.

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 ...

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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 ...

Version 3.1 of Splunk App for VMware Released

Splunk announced the general availability of version 3.1 of the Splunk App for VMware, which provides comprehensive operational visibility into virtualized environments.

This release delivers significant new features, including 200 out-of-the-box reports, the ability to instantly identify outliers for real-time triage and built-in correlation into storage systems including a direct drill down into data from NetApp Data ONTAP.

Features of version 3.1 of the Splunk App for VMware include:

- Operational Performance Insights: Visualize the operational health of the VMware environment with more than 200 out-of-the-box, customizable reports; identify underperforming/distressed hosts, virtual machines (VMs) and data stores in real time.

- Advanced Mapping Visualizations: Access interactive, visual maps of your virtual environment, highlighting problems and statistical comparisons based on predefined, customizable thresholds. Immediately identify outliers that fall outside the normal distribution on a statistical map of your VMware environment.

- NetApp and Storage Correlations: Accelerate troubleshooting, optimize capacity and streamline workloads with out-of-the-box correlation between VMware virtualization layer and storage systems, including direct drill down into data from NetApp Data ONTAP storage for in-depth investigation.

- End-to-End Visibility: Correlate data from virtual infrastructure with data from applications, operating systems, hardware and networks to gain operational intelligence across all technology tiers.

- Advanced Analytics: Forecast future resource CPU, memory and disk requirements on VMware VCenter, ESXi hosts and VMs using various predictive algorithms; gain visibility into potential security breaches and non-compliant usage patterns; explore unique errors and exceptions; track changes with visibility into vCenter tasks.

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 ...