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Netdata Enhances Kubernetes Support

Netdata announced a simplified and visual approach to monitoring Kubernetes.

Users of Netdata Cloud can now easily access the platform's built-in Helm chart to instantly monitor and troubleshoot unlimited numbers of Kubernetes clusters for free in real-time.

By utilizing the Netdata open-source Agent to collect and store metrics from any number of Kubernetes clusters, Netdata Cloud is able to immediately derive real-time insights that are streamed to the platform directly for visual monitoring of Kubernetes workloads with none of the traditional implementation challenges or setup complexity. Through its distributed data architecture, the platform enables DevOps teams to visualize their infrastructure with auto-discovery and zero-configuration in just a few minutes.

"Netdata's commitment to providing users with a free, zero-configuration Kubernetes monitoring experience allows us to meet demands from our community of developers, SRE's, and sysadmins who help us focus product development on what truly matters," said Robin Schumacher, VP of Product at Netdata. "Simple deployment, granular monitoring, and providing full visibility into IT black boxes are all key elements to effective troubleshooting, especially when using Kubernetes to orchestrate distributed systems. Netdata helps everyone be effective at uncovering issues in Kubernetes deployments."

Implementing Kubernetes is a growing practice among technology-focused companies. As the platform continues to build momentum, developers, SRE's and system administrators will need to adapt how they monitor their environment to troubleshoot anomalies and outages. The challenge lies in traditional approaches to Kubernetes support, where current solutions often do not offer the ease of use, depth of metrics, and visualizations needed to ensure healthy Kubernetes clusters.

Kubernetes monitoring with Netdata now:

- Features auto-discovery and metric collection from the node itself, kubelet/kube-proxy, pods/containers, and any containerized services or applications, such as databases and web servers, and then auto-configures visualizations within minutes.

- Removes the black-box feel of traditional Kubernetes monitoring by granting developers, SRE's and system administrators full visibility into their clusters, allowing them to digest all metrics and activity, while troubleshooting anomalies in an easy-to-navigate visual interface.

- Simplifies the deployment process, enabling users to visualize what is going on inside containers from CPU usage to disk IO, without manually setting up charts or writing queries to retrieve data.

Netdata circumvents the complexity and high-cost enterprises typically encounter when monitoring their Kubernetes deployments with a simplified solution with no limits as to the number of nodes, data, or users. The solution also employs a handful of complementary tools and collectors for peeling back the many complex layers of a Kubernetes cluster. These methods work together to give users every metric needed to troubleshoot performance or availability issues across their Kubernetes infrastructure.

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Netdata Enhances Kubernetes Support

Netdata announced a simplified and visual approach to monitoring Kubernetes.

Users of Netdata Cloud can now easily access the platform's built-in Helm chart to instantly monitor and troubleshoot unlimited numbers of Kubernetes clusters for free in real-time.

By utilizing the Netdata open-source Agent to collect and store metrics from any number of Kubernetes clusters, Netdata Cloud is able to immediately derive real-time insights that are streamed to the platform directly for visual monitoring of Kubernetes workloads with none of the traditional implementation challenges or setup complexity. Through its distributed data architecture, the platform enables DevOps teams to visualize their infrastructure with auto-discovery and zero-configuration in just a few minutes.

"Netdata's commitment to providing users with a free, zero-configuration Kubernetes monitoring experience allows us to meet demands from our community of developers, SRE's, and sysadmins who help us focus product development on what truly matters," said Robin Schumacher, VP of Product at Netdata. "Simple deployment, granular monitoring, and providing full visibility into IT black boxes are all key elements to effective troubleshooting, especially when using Kubernetes to orchestrate distributed systems. Netdata helps everyone be effective at uncovering issues in Kubernetes deployments."

Implementing Kubernetes is a growing practice among technology-focused companies. As the platform continues to build momentum, developers, SRE's and system administrators will need to adapt how they monitor their environment to troubleshoot anomalies and outages. The challenge lies in traditional approaches to Kubernetes support, where current solutions often do not offer the ease of use, depth of metrics, and visualizations needed to ensure healthy Kubernetes clusters.

Kubernetes monitoring with Netdata now:

- Features auto-discovery and metric collection from the node itself, kubelet/kube-proxy, pods/containers, and any containerized services or applications, such as databases and web servers, and then auto-configures visualizations within minutes.

- Removes the black-box feel of traditional Kubernetes monitoring by granting developers, SRE's and system administrators full visibility into their clusters, allowing them to digest all metrics and activity, while troubleshooting anomalies in an easy-to-navigate visual interface.

- Simplifies the deployment process, enabling users to visualize what is going on inside containers from CPU usage to disk IO, without manually setting up charts or writing queries to retrieve data.

Netdata circumvents the complexity and high-cost enterprises typically encounter when monitoring their Kubernetes deployments with a simplified solution with no limits as to the number of nodes, data, or users. The solution also employs a handful of complementary tools and collectors for peeling back the many complex layers of a Kubernetes cluster. These methods work together to give users every metric needed to troubleshoot performance or availability issues across their Kubernetes infrastructure.

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Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...