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

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

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Course Overview
- This intensive ‘Kubernetes Monitoring (K8S-MON-108): 1500 Questions’ course is meticulously designed to transform participants into experts in observing, understanding, and optimizing Kubernetes environments. Utilizing a unique “1500 Questions” approach, it ensures a highly interactive and retention-focused learning experience, challenging students to apply their knowledge to a vast array of real-world scenarios and edge cases. Beyond theoretical concepts, the curriculum plunges deep into the practical application of industry-leading monitoring tools and methodologies. This course isn’t just about knowing the tools; it’s about mastering the art of asking the right questions, interpreting complex data, and proactively solving problems within dynamic, containerized infrastructures. It covers the full spectrum of observability, from collecting granular metrics to comprehensive log management and insightful dashboard creation, all while focusing on performance tuning strategies crucial for maintaining highly available and efficient Kubernetes clusters.
Requirements / Prerequisites
- Fundamental Kubernetes Understanding: Participants should possess a working knowledge of core Kubernetes concepts, including pods, deployments, services, namespaces, and basic resource management. Familiarity with kubectl for interacting with the cluster is essential.
- Command-Line Proficiency: Comfort navigating and executing commands within a Linux-based terminal environment is expected, as much of the practical work involves interacting with command-line tools and configuration files.
- Basic Networking Concepts: A foundational understanding of IP addresses, ports, DNS, and how services communicate within a network is beneficial for comprehending service discovery and network-related metrics in a Kubernetes context.
- Problem-Solving Acumen: An analytical mindset and a proactive approach to troubleshooting are key, as the course heavily emphasizes diagnosing issues and optimizing performance through data interpretation.
Skills Covered / Tools Used
- Mastering Prometheus: Gain deep expertise in Prometheus architecture, installation, configuration, service discovery mechanisms (e.g., kubernetes_sd_configs), and custom metric exposition. Learn to write advanced PromQL queries for data aggregation, filtering, and analysis, creating robust monitoring rules and recording rules for efficient data processing of Kubernetes components.
- Advanced Grafana Dashboarding: Develop the ability to design, build, and customize sophisticated Grafana dashboards from scratch. Explore various panel types, leverage template variables for dynamic dashboard filtering, integrate multiple data sources, and implement alert conditions directly within Grafana for immediate operational visibility into Kubernetes clusters and applications.
- Operationalizing metrics-server: Understand the critical role of metrics-server in Kubernetes, how it collects cluster-wide resource usage metrics (CPU, memory), and its fundamental integration with Horizontal Pod Autoscalers (HPAs) and Vertical Pod Autoscalers (VPAs) for automated scaling decisions based on real-time data.
- Designing Effective Alerting Strategies: Learn to implement comprehensive alerting systems using Prometheus Alertmanager. Configure alert rules with thresholds, severity levels, and grouping strategies. Master integrating Alertmanager with various notification channels (e.g., Slack, PagerDuty, email) and best practices for creating actionable, non-fatiguing alerts specific to Kubernetes health.
- Comprehensive ELK/EFK Logging: Dive into robust log aggregation and analysis using the Elasticsearch, Logstash/Fluentd/Fluent Bit, and Kibana (ELK/EFK) stack. Understand how to collect, parse, transform, store, and visualize Kubernetes application and system logs, enabling quick diagnosis of issues and auditing capabilities within dynamic environments.
- Kubernetes Performance Tuning: Acquire practical skills in identifying performance bottlenecks within Kubernetes clusters and applications. Utilize monitoring data to analyze resource utilization, optimize pod resource requests and limits, implement efficient autoscaling policies (HPA, VPA, KEDA), and diagnose network or storage-related performance issues to enhance cluster efficiency.
- SLI/SLO Implementation: Learn to define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) relevant to Kubernetes applications and infrastructure. Implement monitoring to track SLI adherence and manage error budgets, fostering a culture of reliability engineering and ensuring service quality.
- Troubleshooting with Observability: Develop a systematic approach to troubleshooting Kubernetes incidents using the rich data provided by metrics, logs, and alerts. Practice root cause analysis techniques, leveraging dashboards and queries to pinpoint problems rapidly and efficiently in complex distributed systems.
Benefits / Outcomes
- Build Robust Monitoring Solutions: Upon completion, you will be equipped to design, implement, and maintain highly effective and scalable monitoring and logging solutions for complex Kubernetes environments, ensuring application stability and performance.
- Proactive Problem Resolution: Gain the ability to anticipate and proactively identify potential issues within your Kubernetes clusters and applications, leveraging advanced alerting and insightful dashboards to prevent outages before they impact users.
- Optimize Kubernetes Performance: Master the techniques for analyzing performance data, identifying resource inefficiencies, and implementing strategies for performance tuning, leading to significant cost savings and improved cluster utilization.
- Enhanced Troubleshooting Capability: Develop a systematic and data-driven approach to troubleshooting Kubernetes-related incidents, significantly reducing mean time to resolution (MTTR) for critical issues.
- Confidently Manage Observability: Speak authoritatively about Kubernetes observability best practices, architecture, and toolchains, positioning yourself as a go-to expert within your team or organization.
- Career Advancement: The comprehensive, hands-on knowledge gained in this course is directly applicable to roles such as Site Reliability Engineer (SRE), DevOps Engineer, Cloud Engineer, and Kubernetes Administrator, significantly enhancing your professional profile.
PROS
- Intensive Question-Driven Format: The “1500 Questions” approach provides unparalleled depth of understanding and practical application, forcing students to critically engage with the material and master complex scenarios. This fosters a highly active learning environment, ensuring concepts are not just learned but truly internalized through problem-solving.
- Comprehensive Tool Coverage: The course meticulously covers all critical components of a modern Kubernetes observability stack, including Prometheus, Grafana, metrics-server, Alertmanager, and the ELK/EFK stack, providing a holistic and integrated view rather than fragmented tool-specific knowledge.
- Direct Focus on Performance Tuning: Beyond just monitoring, a significant portion of the curriculum is dedicated to leveraging monitoring data for actual performance optimization, resource efficiency, and advanced autoscaling, which is invaluable for production Kubernetes environments.
- Personalized Learning Experience: With a limited class size of 13 students, participants can expect more personalized attention, direct interaction with the instructor, and opportunities for in-depth discussions on specific challenges they face, ensuring a tailored educational journey.
CONS
- Potentially Overwhelming Intensity: The sheer volume and question-centric nature of the course, while a strength for some, might be perceived as very demanding and intense for learners who prefer a slower pace or a more lecture-driven approach, requiring significant dedication and self-study outside of scheduled sessions.