Kubernetes Monitoring (K8S-MON-108): 1500 Questions

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Covers Prometheus, Grafana, metrics-server, alerts, dashboards, ELK/EFK logging & performance tuning
πŸ‘₯ 13 students

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  • Course Overview
    • Course Title: Kubernetes Monitoring (K8S-MON-108): 1500 Questions
    • Course Caption: Covers Prometheus, Grafana, metrics-server, alerts, dashboards, ELK/EFK logging & performance tuning
    • This intensive K8S-MON-108 course employs a unique “1500 Questions” approach, deeply embedding practical problem-solving to master Kubernetes monitoring. It moves beyond theory, focusing on hands-on application and critical thinking to build robust observability into your cloud-native environments.
    • Participants gain comprehensive expertise across the entire monitoring stack: proficiently utilizing Prometheus for metrics collection and advanced PromQL, creating insightful dashboards with Grafana, and understanding metrics-server for core K8s resource visibility. The curriculum also extensively covers implementing and managing powerful ELK/EFK logging solutions, ensuring complete visibility into application and cluster events.
    • Significant emphasis is placed on proactive performance tuning and effective troubleshooting. You’ll learn to identify bottlenecks, optimize resource usage, and respond rapidly to incidents, ensuring high availability and operational efficiency. Designed for DevOps engineers, SREs, and K8s administrators, this course equips you with diagnostic skills for resilient Kubernetes systems.
  • Requirements / Prerequisites
    • Core Kubernetes Understanding: Familiarity with basic K8s concepts (pods, deployments, services, namespaces) and `kubectl` operations.
    • Linux Command Line: Proficiency with shell commands, file system navigation, and basic scripting.
    • Networking Basics: Conceptual knowledge of IP, ports, DNS, and HTTP/HTTPS.
    • YAML Fluency: Ability to read and modify Kubernetes and monitoring tool configurations defined in YAML.
    • Distributed Systems Awareness: A foundational understanding of challenges in distributed environments.
  • Skills Covered / Tools Used
    • Prometheus Ecosystem Mastery:
      • Prometheus Core & PromQL: Grasping data models, scrape mechanisms, and mastering advanced PromQL for complex, real-time data analysis, including aggregation, vector matching, and subqueries.
      • K8s Service Discovery & Exporters: Configuring Prometheus to auto-discover Kubernetes targets, deploying essential exporters like node_exporter and cAdvisor, and understanding custom metric sources.
      • Alertmanager & Rules: Designing sophisticated alerting rules, managing alert routing, inhibition, silences, and integrating with various notification platforms for timely incident response.
    • Grafana Dashboard & Visualization:
      • Data Source Integration & Design: Connecting Grafana to Prometheus, Elasticsearch; building dynamic, interactive dashboards with variables, templating, and diverse panel types.
      • Grafana Alerting & Management: Setting up direct Grafana alerts, defining thresholds, and managing dashboard permissions for organized monitoring views.
    • Kubernetes Metrics-server Utilization:
      • Installation, Operation & HPA/VPA: Understanding metrics-server’s role, its deployment, and leveraging `kubectl top` and resource metrics for Horizontal Pod Autoscaling (HPA) and Vertical Pod Autoscaling (VPA).
    • ELK/EFK Stack for Logging:
      • Log Collection & Analysis: Implementing robust log aggregation with Fluentd/Fluentbit from K8s, including parsing and forwarding to Elasticsearch. Using Kibana for powerful log exploration, error analysis, and advanced KQL querying.
    • Performance Tuning & Troubleshooting Methodologies:
      • Bottleneck Identification & Resource Optimization: Systematically diagnosing CPU, memory, network, I/O issues. Best practices for setting intelligent resource requests and limits in Kubernetes.
      • Golden Signals & RCA: Applying the Golden Signals (Latency, Traffic, Errors, Saturation) for holistic monitoring and executing effective Root Cause Analysis (RCA) using collected observability data.
      • Capacity Planning: Utilizing historical monitoring trends to project future resource requirements and inform strategic infrastructure decisions.
  • Benefits / Outcomes
    • Proactive Incident Prevention: Develop systems to detect anomalies before they impact users, significantly reducing downtime.
    • Accelerated Troubleshooting: Drastically reduce Mean Time To Resolution (MTTR) by efficiently diagnosing and resolving complex Kubernetes issues.
    • Optimized Resource Utilization: Implement data-driven strategies to right-size cluster resources, leading to significant cost savings and improved performance.
    • Enhanced System Reliability: Build and maintain highly stable and available Kubernetes environments through continuous monitoring and informed decision-making.
    • Career Advancement: Gain highly sought-after expertise in cloud-native monitoring, positioning you as a critical asset in modern infrastructure teams.
  • PROS
    • “1500 Questions” Approach: Unparalleled hands-on learning through extensive problem-solving.
    • Comprehensive Tool Coverage: Master essential Kubernetes monitoring tools from metrics to logs.
    • Practical & Real-world Focus: Gain immediately applicable skills for complex cloud-native environments.
    • Small Class Size: Benefit from personalized attention and focused instruction (13 students).
  • CONS
    • Intensive Workload: The “1500 Questions” format demands significant time commitment and active engagement.
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