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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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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 ...

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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 ...

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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 ...

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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 ...