[{"data":1,"prerenderedAt":833},["ShallowReactive",2],{"blog-en-grafana-dashboard-kubernetes-observability":3,"blog-related-en-grafana-dashboard-kubernetes-observability":786,"blog-en-grafana-dashboard-kubernetes-observability-alt":226},{"id":4,"title":5,"author":6,"body":7,"date":768,"description":769,"extension":770,"image":192,"locale":771,"meta":772,"navigation":226,"path":773,"seo":774,"stem":775,"tags":776,"__hash__":785},"blog\u002Fblog\u002Fen\u002Fgrafana-dashboard-kubernetes-observability.md","Grafana Dashboard Design: Kubernetes Observability in Practice","Kubo Team",{"type":8,"value":9,"toc":743},"minimark",[10,28,33,47,50,72,75,137,146,155,159,174,179,182,193,197,200,257,261,264,268,271,275,278,284,288,296,300,306,310,340,344,347,375,384,388,392,401,421,425,433,578,581,588,592,596,603,629,633,641,667,671,677,680,684,687,718,727,739],[11,12,13,14,21,22,27],"p",{},"Collecting metrics with Prometheus is only half the battle -- if the data is not visualized in a way that humans can quickly interpret, monitoring loses much of its value. ",[15,16,20],"a",{"href":17,"rel":18},"https:\u002F\u002Fgrafana.com\u002F",[19],"nofollow","Grafana"," is the de facto visualization standard in the CNCF ecosystem and the core tool for achieving Kubernetes observability. On lightweight K3s-based platforms like ",[15,23,26],{"href":24,"rel":25},"https:\u002F\u002Fkubo.hexabase.io\u002F",[19],"Kubo",", the Grafana + Prometheus stack is the most common monitoring solution. This article covers practical dashboard design techniques and best practices that deliver real value in production.",[29,30,32],"h2",{"id":31},"the-three-pillars-of-observability-and-dashboard-strategy","The Three Pillars of Observability and Dashboard Strategy",[11,34,35,36,41,42,46],{},"Traditional system-level monitoring is no longer sufficient for modern Kubernetes environments. As noted in the ",[15,37,40],{"href":38,"rel":39},"https:\u002F\u002Fsupport.tools\u002Fkubernetes-observability-best-practices-2025\u002F",[19],"Kubernetes Observability Best Practices 2025"," guide, a comprehensive approach requires the three pillars of observability: ",[43,44,45],"strong",{},"Metrics, Logs, and Traces",".",[11,48,49],{},"Grafana serves as a unified platform for all three pillars:",[51,52,53,60,66],"ul",{},[54,55,56,59],"li",{},[43,57,58],{},"Metrics",": Connect to Prometheus, Mimir, InfluxDB, and other data sources",[54,61,62,65],{},[43,63,64],{},"Logs",": Search and visualize logs through Loki",[54,67,68,71],{},[43,69,70],{},"Traces",": Display trace data from Tempo or Jaeger",[11,73,74],{},"Start your dashboard design with these established frameworks:",[76,77,78,94],"table",{},[79,80,81],"thead",{},[82,83,84,88,91],"tr",{},[85,86,87],"th",{},"Framework",[85,89,90],{},"Target",[85,92,93],{},"Signals",[95,96,97,111,124],"tbody",{},[82,98,99,105,108],{},[100,101,102],"td",{},[43,103,104],{},"RED Method",[100,106,107],{},"Services (request-driven)",[100,109,110],{},"Rate, Errors, Duration",[82,112,113,118,121],{},[100,114,115],{},[43,116,117],{},"USE Method",[100,119,120],{},"Resources (infrastructure)",[100,122,123],{},"Utilization, Saturation, Errors",[82,125,126,131,134],{},[100,127,128],{},[43,129,130],{},"Four Golden Signals",[100,132,133],{},"General purpose",[100,135,136],{},"Latency, Traffic, Errors, Saturation",[11,138,139,140,145],{},"As the ",[15,141,144],{"href":142,"rel":143},"https:\u002F\u002Fgrafana.com\u002Fdocs\u002Fgrafana\u002Flatest\u002Fdashboards\u002Fbuild-dashboards\u002Fbest-practices\u002F",[19],"official Grafana best practices"," emphasize, dashboards should \"tell a story\" -- design a logical data progression from general overview to specific details.",[11,147,148,149,154],{},"With ",[15,150,153],{"href":151,"rel":152},"https:\u002F\u002Fwww.hexabase.com\u002Fproduct\u002Fcaptain-ai\u002F",[19],"Captain.AI",", AI can analyze dashboard data to help with early anomaly detection and root cause identification.",[29,156,158],{"id":157},"five-essential-dashboards-and-panel-layout","Five Essential Dashboards and Panel Layout",[11,160,161,162,167,168,173],{},"Drawing from the ",[15,163,166],{"href":164,"rel":165},"https:\u002F\u002Fwww.skedler.com\u002Fblog\u002F10-must-have-grafana-dashboards-kubernetes-prometheus\u002F",[19],"Skedler guide"," and ",[15,169,172],{"href":170,"rel":171},"https:\u002F\u002Fwww.apptio.com\u002Ftopics\u002Fkubernetes\u002Fmonitoring\u002Fgrafana-dashboard\u002F",[19],"Apptio's Kubernetes guide",", here are the dashboards every Kubernetes environment needs.",[175,176,178],"h3",{"id":177},"_1-cluster-overview-dashboard","1. Cluster Overview Dashboard",[11,180,181],{},"A top-level dashboard for at-a-glance cluster health:",[183,184,189],"pre",{"className":185,"code":187,"language":188},[186],"language-text","Row 1: Stat Panels\n  - total nodes - ready nodes\n  - total pods - running pods\n  - Cluster CPU Utilization\n  - Cluster Memory Utilization\n\nRow 2: Time Series Panels\n  - CPU Usage by Node\n  - Memory Usage by Node\n\nRow 3: Table Panel\n  - Resource Usage by Namespace\n","text",[190,191,187],"code",{"__ignoreMap":192},"",[175,194,196],{"id":195},"_2-node-resource-dashboard","2. Node Resource Dashboard",[11,198,199],{},"Visualize Node Exporter metrics for node-level resource monitoring:",[183,201,205],{"className":202,"code":203,"language":204,"meta":192,"style":192},"language-promql shiki shiki-themes tokyo-night","# CPU usage\n100 - (avg by(instance)(irate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)\n\n# Memory usage\n(1 - node_memory_MemAvailable_bytes \u002F node_memory_MemTotal_bytes) * 100\n\n# Disk usage\n(1 - node_filesystem_avail_bytes{mountpoint=\"\u002F\"} \u002F node_filesystem_size_bytes{mountpoint=\"\u002F\"}) * 100\n","promql",[190,206,207,215,221,228,234,240,245,251],{"__ignoreMap":192},[208,209,212],"span",{"class":210,"line":211},"line",1,[208,213,214],{},"# CPU usage\n",[208,216,218],{"class":210,"line":217},2,[208,219,220],{},"100 - (avg by(instance)(irate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)\n",[208,222,224],{"class":210,"line":223},3,[208,225,227],{"emptyLinePlaceholder":226},true,"\n",[208,229,231],{"class":210,"line":230},4,[208,232,233],{},"# Memory usage\n",[208,235,237],{"class":210,"line":236},5,[208,238,239],{},"(1 - node_memory_MemAvailable_bytes \u002F node_memory_MemTotal_bytes) * 100\n",[208,241,243],{"class":210,"line":242},6,[208,244,227],{"emptyLinePlaceholder":226},[208,246,248],{"class":210,"line":247},7,[208,249,250],{},"# Disk usage\n",[208,252,254],{"class":210,"line":253},8,[208,255,256],{},"(1 - node_filesystem_avail_bytes{mountpoint=\"\u002F\"} \u002F node_filesystem_size_bytes{mountpoint=\"\u002F\"}) * 100\n",[175,258,260],{"id":259},"_3-pod-deployment-dashboard","3. Pod \u002F Deployment Dashboard",[11,262,263],{},"Focus on namespaces and workloads to monitor application health and resource consumption per deployment.",[175,265,267],{"id":266},"_4-network-dashboard","4. Network Dashboard",[11,269,270],{},"Visualize network metrics including inter-pod communication, Ingress traffic, and DNS query rates.",[175,272,274],{"id":273},"_5-alert-overview-dashboard","5. Alert Overview Dashboard",[11,276,277],{},"Display currently firing alerts and alert history, serving as the starting point for incident response.",[11,279,280,281,46],{},"These dashboards should be deployed as the standard monitoring set for clusters running on ",[15,282,26],{"href":24,"rel":283},[19],[29,285,287],{"id":286},"template-variables-and-interactive-design","Template Variables and Interactive Design",[11,289,290,291,295],{},"As ",[15,292,294],{"href":142,"rel":293},[19],"Grafana's official documentation"," recommends, template variables dramatically improve dashboard reusability and reduce dashboard sprawl.",[175,297,299],{"id":298},"variable-definitions","Variable Definitions",[183,301,304],{"className":302,"code":303,"language":188},[186],"$cluster   - Data source switching (multi-cluster support)\n$namespace - Namespace filter\n$workload  - Deployment \u002F StatefulSet \u002F DaemonSet\n$pod       - Pod name filter\n$interval  - Auto-adjusting time interval\n",[190,305,303],{"__ignoreMap":192},[175,307,309],{"id":308},"dynamic-filtering-implementation","Dynamic Filtering Implementation",[183,311,313],{"className":202,"code":312,"language":204,"meta":192,"style":192},"# Query using variables\nsum(rate(container_cpu_usage_seconds_total{\n  namespace=~\"$namespace\",\n  pod=~\"$pod\"\n}[5m])) by (pod)\n",[190,314,315,320,325,330,335],{"__ignoreMap":192},[208,316,317],{"class":210,"line":211},[208,318,319],{},"# Query using variables\n",[208,321,322],{"class":210,"line":217},[208,323,324],{},"sum(rate(container_cpu_usage_seconds_total{\n",[208,326,327],{"class":210,"line":223},[208,328,329],{},"  namespace=~\"$namespace\",\n",[208,331,332],{"class":210,"line":230},[208,333,334],{},"  pod=~\"$pod\"\n",[208,336,337],{"class":210,"line":236},[208,338,339],{},"}[5m])) by (pod)\n",[175,341,343],{"id":342},"drill-down-design","Drill-Down Design",[11,345,346],{},"Design a hierarchical dashboard structure that enables smooth transitions from overview to detail:",[348,349,350,359,367],"ol",{},[54,351,352,355,356],{},[43,353,354],{},"Cluster Overview"," -- click node name to navigate to ",[43,357,358],{},"Node Detail",[54,360,361,363,364],{},[43,362,358],{}," -- click namespace to navigate to ",[43,365,366],{},"Namespace Detail",[54,368,369,371,372],{},[43,370,366],{}," -- click pod name to navigate to ",[43,373,374],{},"Pod Detail",[11,376,377,378,383],{},"Use panel links and data links so users can navigate intuitively. ",[15,379,382],{"href":380,"rel":381},"https:\u002F\u002Fwww.managekubernetes.com\u002Fblog\u002Fgrafana-dashboards-for-kubernetes",[19],"ManageKubernetes.com"," explains that drill-down structures significantly reduce incident response time.",[29,385,387],{"id":386},"grafana-12-features-and-dashboard-as-code","Grafana 12 Features and Dashboard as Code",[175,389,391],{"id":390},"grafana-12-highlights","Grafana 12 Highlights",[11,393,394,395,400],{},"Announced at ",[15,396,399],{"href":397,"rel":398},"https:\u002F\u002Fgrafana.com\u002Fevents\u002Fobservabilitycon\u002F2025\u002Fhands-on-labs\u002Fbest-practices-to-level-up-your-grafana-dashboarding-skills\u002F",[19],"GrafanaCON 2025",", Grafana 12 brings major improvements to dashboard design:",[51,402,403,409,415],{},[54,404,405,408],{},[43,406,407],{},"Tabs",": Segment data by context, enabling multiple viewpoints within a single dashboard",[54,410,411,414],{},[43,412,413],{},"Conditional Rendering",": Control panel visibility based on specific conditions, reducing visual clutter",[54,416,417,420],{},[43,418,419],{},"AI-Assisted Anomaly Highlighting",": Automatically emphasize anomalous metric values",[175,422,424],{"id":423},"dashboard-as-code","Dashboard as Code",[11,426,139,427,432],{},[15,428,431],{"href":429,"rel":430},"https:\u002F\u002Fbix-tech.com\u002Ftechnical-dashboards-with-grafana-and-prometheus-a-practical-nofluff-guide\u002F",[19],"BIX Tech guide"," recommends, version-controlling dashboard configurations as code makes change tracking and rollback straightforward:",[183,434,438],{"className":435,"code":436,"language":437,"meta":192,"style":192},"language-yaml shiki shiki-themes tokyo-night","# Grafana Operator CRD management\napiVersion: grafana.integreatly.org\u002Fv1beta1\nkind: GrafanaDashboard\nmetadata:\n  name: cluster-overview\n  namespace: monitoring\nspec:\n  resyncPeriod: 30s\n  instanceSelector:\n    matchLabels:\n      dashboards: grafana\n  json: |\n    {\n      \"title\": \"Cluster Overview\",\n      \"panels\": [...]\n    }\n","yaml",[190,439,440,446,460,470,478,488,498,505,515,523,531,542,554,560,566,572],{"__ignoreMap":192},[208,441,442],{"class":210,"line":211},[208,443,445],{"class":444},"sbD-w","# Grafana Operator CRD management\n",[208,447,448,452,456],{"class":210,"line":217},[208,449,451],{"class":450},"s0U2E","apiVersion",[208,453,455],{"class":454},"sAklC",":",[208,457,459],{"class":458},"sPY7s"," grafana.integreatly.org\u002Fv1beta1\n",[208,461,462,465,467],{"class":210,"line":223},[208,463,464],{"class":450},"kind",[208,466,455],{"class":454},[208,468,469],{"class":458}," GrafanaDashboard\n",[208,471,472,475],{"class":210,"line":230},[208,473,474],{"class":450},"metadata",[208,476,477],{"class":454},":\n",[208,479,480,483,485],{"class":210,"line":236},[208,481,482],{"class":450},"  name",[208,484,455],{"class":454},[208,486,487],{"class":458}," cluster-overview\n",[208,489,490,493,495],{"class":210,"line":242},[208,491,492],{"class":450},"  namespace",[208,494,455],{"class":454},[208,496,497],{"class":458}," monitoring\n",[208,499,500,503],{"class":210,"line":247},[208,501,502],{"class":450},"spec",[208,504,477],{"class":454},[208,506,507,510,512],{"class":210,"line":253},[208,508,509],{"class":450},"  resyncPeriod",[208,511,455],{"class":454},[208,513,514],{"class":458}," 30s\n",[208,516,518,521],{"class":210,"line":517},9,[208,519,520],{"class":450},"  instanceSelector",[208,522,477],{"class":454},[208,524,526,529],{"class":210,"line":525},10,[208,527,528],{"class":450},"    matchLabels",[208,530,477],{"class":454},[208,532,534,537,539],{"class":210,"line":533},11,[208,535,536],{"class":450},"      dashboards",[208,538,455],{"class":454},[208,540,541],{"class":458}," grafana\n",[208,543,545,548,550],{"class":210,"line":544},12,[208,546,547],{"class":450},"  json",[208,549,455],{"class":454},[208,551,553],{"class":552},"sd1Qi"," |\n",[208,555,557],{"class":210,"line":556},13,[208,558,559],{"class":458},"    {\n",[208,561,563],{"class":210,"line":562},14,[208,564,565],{"class":458},"      \"title\": \"Cluster Overview\",\n",[208,567,569],{"class":210,"line":568},15,[208,570,571],{"class":458},"      \"panels\": [...]\n",[208,573,575],{"class":210,"line":574},16,[208,576,577],{"class":458},"    }\n",[11,579,580],{},"Alternatively, using Jsonnet and the Grafonnet library for programmatic dashboard generation is highly effective. The \"GitOps for Dashboards\" approach -- managing in Git and deploying through ci-cd pipelines -- has gained wide adoption.",[11,582,583,584,587],{},"By integrating with ",[15,585,153],{"href":151,"rel":586},[19],", you can enable automatic dashboard generation and AI-driven operational recommendations based on monitoring data.",[29,589,591],{"id":590},"performance-optimization-and-best-practices","Performance Optimization and Best Practices",[175,593,595],{"id":594},"reducing-cognitive-load","Reducing Cognitive Load",[11,597,598,599,455],{},"Design principles based on ",[15,600,602],{"href":142,"rel":601},[19],"Grafana's official best practices",[51,604,605,611,617,623],{},[54,606,607,610],{},[43,608,609],{},"One dashboard = one purpose",": Do not pack multiple concerns into a single dashboard",[54,612,613,616],{},[43,614,615],{},"Place the most important KPIs in the top-left",": Align with natural eye movement",[54,618,619,622],{},[43,620,621],{},"Consistent color usage",": Blue = normal, yellow = warning, red = critical",[54,624,625,628],{},[43,626,627],{},"Set thresholds",": Configure thresholds on panels so anomalies are visually obvious at a glance",[175,630,632],{"id":631},"performance-optimization","Performance Optimization",[11,634,635,636,455],{},"Following guidance from ",[15,637,640],{"href":638,"rel":639},"https:\u002F\u002Fwww.groundcover.com\u002Flearn\u002Fobservability\u002Fgrafana-dashboards",[19],"groundcover",[51,642,643,649,655,661],{},[54,644,645,648],{},[43,646,647],{},"Set appropriate refresh intervals",": Match data update frequency (30s to 5m)",[54,650,651,654],{},[43,652,653],{},"Limit time ranges",": Set sensible default time ranges to prevent excessive data retrieval",[54,656,657,660],{},[43,658,659],{},"Use Recording Rules",": Pre-compute in Prometheus to reduce query load",[54,662,663,666],{},[43,664,665],{},"Optimize panel count",": Target 20-30 panels per dashboard",[175,668,670],{"id":669},"naming-conventions-and-governance","Naming Conventions and Governance",[183,672,675],{"className":673,"code":674,"language":188},[186],"# Recommended naming convention\n{team-name} \u002F {category} \u002F {dashboard-name}\ne.g.: platform\u002Fkubernetes\u002Fcluster-overview\ne.g.: app-team\u002Fapi\u002Fservice-health\n\n# Test prefixes\nTEST: {name} - Dashboard under testing\nTMP: {name}  - Temporary dashboard\n",[190,676,674],{"__ignoreMap":192},[11,678,679],{},"Use folder structures to organize dashboards by team, and configure RBAC for appropriate access controls.",[29,681,683],{"id":682},"conclusion","Conclusion",[11,685,686],{},"Effective Grafana dashboard design is the cornerstone of Kubernetes observability. The key takeaways from this article are:",[348,688,689,695,701,707,712],{},[54,690,691,694],{},[43,692,693],{},"RED \u002F USE \u002F Four Golden Signals"," frameworks for selecting the right metrics",[54,696,697,700],{},[43,698,699],{},"Five essential dashboards"," for comprehensive monitoring coverage",[54,702,703,706],{},[43,704,705],{},"Template variables and drill-downs"," for interactive, reusable design",[54,708,709,711],{},[43,710,424],{}," for version control and reproducibility",[54,713,714,717],{},[43,715,716],{},"Cognitive load reduction and performance optimization"," for operational efficiency",[11,719,720,723,724,46],{},[15,721,26],{"href":24,"rel":722},[19]," is built on K3s with strong affinity for Grafana and Prometheus, enabling you to build a powerful observability foundation with minimal configuration. If you are looking for a cloud-native monitoring and visualization solution, explore ",[15,725,26],{"href":24,"rel":726},[19],[11,728,729,730,733,734,46],{},"For AI-powered operational support, discover the intelligent Kubernetes operations solutions offered by ",[15,731,153],{"href":151,"rel":732},[19],". For consultations, please reach out through our ",[15,735,738],{"href":736,"rel":737},"https:\u002F\u002Fwww.hexabase.com\u002Fcontact-us\u002F",[19],"contact page",[740,741,742],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html pre.shiki code .sbD-w, html code.shiki .sbD-w{--shiki-default:#51597D;--shiki-default-font-style:italic}html pre.shiki code .s0U2E, html code.shiki .s0U2E{--shiki-default:#F7768E}html pre.shiki code .sAklC, html code.shiki .sAklC{--shiki-default:#89DDFF}html pre.shiki code .sPY7s, html code.shiki .sPY7s{--shiki-default:#9ECE6A}html pre.shiki code .sd1Qi, html code.shiki .sd1Qi{--shiki-default:#BB9AF7}",{"title":192,"searchDepth":217,"depth":217,"links":744},[745,746,753,758,762,767],{"id":31,"depth":217,"text":32},{"id":157,"depth":217,"text":158,"children":747},[748,749,750,751,752],{"id":177,"depth":223,"text":178},{"id":195,"depth":223,"text":196},{"id":259,"depth":223,"text":260},{"id":266,"depth":223,"text":267},{"id":273,"depth":223,"text":274},{"id":286,"depth":217,"text":287,"children":754},[755,756,757],{"id":298,"depth":223,"text":299},{"id":308,"depth":223,"text":309},{"id":342,"depth":223,"text":343},{"id":386,"depth":217,"text":387,"children":759},[760,761],{"id":390,"depth":223,"text":391},{"id":423,"depth":223,"text":424},{"id":590,"depth":217,"text":591,"children":763},[764,765,766],{"id":594,"depth":223,"text":595},{"id":631,"depth":223,"text":632},{"id":669,"depth":223,"text":670},{"id":682,"depth":217,"text":683},"2026-05-27","Design effective Grafana dashboards for Kubernetes observability using the RED method, Four Golden Signals, and production-proven best practices.","md","en",{},"\u002Fblog\u002Fen\u002Fgrafana-dashboard-kubernetes-observability",{"title":5,"description":769},"blog\u002Fen\u002Fgrafana-dashboard-kubernetes-observability",[777,778,779,780,781,782,783,784],"grafana","kubernetes","observability","dashboard","cncf","prometheus","monitoring","visualization","_Mb2QvsyfARKWILEdFezUqqyzrP3mRE5kTDAceL3Y1A",[787,793,801,808,815,823],{"path":788,"title":789,"description":790,"date":768,"tags":791},"\u002Fblog\u002Fen\u002Fprometheus-monitoring-kubernetes-guide","The Complete Guide to Monitoring Kubernetes with Prometheus","Master Kubernetes monitoring with Prometheus: from installation and PromQL queries to Alertmanager configuration and production best practices.",[782,778,783,781,779,204,792],"alertmanager",{"path":794,"title":795,"description":796,"date":797,"tags":798},"\u002Fblog\u002Fen\u002Fk3s-servicemonitor-prometheus-not-scraping-connection-points","Your ServiceMonitor YAML Is Correct. So Why Isn't Prometheus Scraping It? A K3s Diagnostic Guide to the 'Connection Points'","You configured a kubernetes service monitor on K3s, but nothing shows up in Prometheus Targets. 90% of the time, the cause is one of four connection points: label selectors, port names, RBAC, or the namespaceSelector. Here's a step-by-step diagnostic guide.","2026-08-30",[799,778,782,800,783,779],"k3s","service-monitor",{"path":802,"title":803,"description":804,"date":805,"tags":806},"\u002Fblog\u002Fen\u002Fkubernetes-ebpf-inspektor-gadget-observability","No strace. No Sidecars. Still Debuggable: Inspektor Gadget's Answer for Seeing Inside Kubernetes with eBPF","When you can't add a privileged container or inject a sidecar into a production Kubernetes cluster, how do you diagnose Pod traffic and syscalls? We explain how the eBPF tool Inspektor Gadget works, and what happened to it in 2026.","2026-08-15",[799,778,807,779,781],"ebpf",{"path":809,"title":810,"description":811,"date":812,"tags":813},"\u002Fblog\u002Fen\u002Fkubernetes-servicemonitor-silent-metrics-failure","Prometheus Is Running. The Metrics Aren't. Three Reasons Your ServiceMonitor Fails Silently","Your kubernetes service monitor isn't being scraped and there's no error to explain why. The root cause is almost always one of three things: label mismatches, a missing namespaceSelector, or RBAC. Here's how to diagnose each one against the official docs.","2026-08-14",[799,778,782,779,814],"servicemonitor",{"path":816,"title":817,"description":818,"date":819,"tags":820},"\u002Fblog\u002Fen\u002Fedge-k3s-observability-homelab-dashboard","A Wall-Mounted Dashboard Taught Me What 'Peace of Mind' Really Means: Observability Design Lessons for Edge K3s Clusters","The trial-and-error troubleshooting behind a home-lab wall-mounted dashboard is the same trap that hits edge Kubernetes clusters scattered across factories and stores. Drawing on official K3s, Prometheus, and Grafana docs, this piece lays out design principles for not deferring observability.","2026-07-26",[799,778,821,779,783,822],"edge-computing","managed-kubernetes",{"path":824,"title":825,"description":826,"date":768,"tags":827},"\u002Fblog\u002Fen\u002Fcncf-graduated-projects-2025-overview","CNCF Graduated Projects 2025: Production-Ready Open Source You Can Trust","A complete guide to all 36 CNCF Graduated projects. From Kubernetes and Prometheus to Cilium and Crossplane, explore production-proven OSS by category.",[781,828,829,830,778,782,831,832],"graduated","cloud-native","oss","cilium","opentelemetry",1792488900536]