Grafana & Prometheus for Metrics-Driven Observability

The Grafana & Prometheus for Metrics-Driven Observability course is designed to help learners build the practical skills required to collect, query, visualize, and act on metrics for effective observability. Tailored for DevOps engineers, cloud engineers, platform engineers, SREs, Kubernetes administrators, and developers, this course provides a comprehensive introduction to metrics-driven observability using Prometheus and Grafana. Through conceptual lessons, guided demonstrations, and hands-on exercises, you'll learn how to instrument applications, collect and query metrics, build meaningful dashboards, and create effective alerts for modern cloud-native environments.

Throughout the course, you'll gain practical experience working with Prometheus, Grafana, PromQL, Kubernetes metrics, OpenTelemetry, and the kube-prometheus-stack. You'll explore the fundamentals of metrics, the Four Golden Signals of Monitoring, and service-level concepts such as SLIs, SLOs, and SLAs. You'll learn how Prometheus collects and stores metrics, work with metric types including counters, gauges, histograms, and summaries, and query data using PromQL.

As you progress, you'll learn how to instrument applications and expose meaningful metrics through the /metrics endpoint. You'll explore both in-code instrumentation and auto-instrumentation concepts with OpenTelemetry, while also learning how to design scalable metric systems and avoid common issues such as high-cardinality metrics.

You'll then put your skills into practice by building Grafana dashboards that transform raw metrics into actionable insights. You'll create time series visualizations, Golden Signal dashboards, histogram and heatmap visualizations, KPI panels, state timelines, and saturation panels to effectively monitor application and infrastructure health.

The course also covers alerting philosophy and Grafana Alerting, helping you understand how to create alerts that provide meaningful signals without generating unnecessary noise. You'll additionally explore metrics observability for AI inference workloads, giving you insight into how observability principles can be applied to modern AI-powered systems.

By the end of this course, you'll have the practical knowledge needed to design and implement a metrics-driven observability solution using Prometheus and Grafana. You'll be able to instrument applications, collect and query metrics, build effective dashboards, identify performance and reliability issues, and create actionable alerts for cloud-native and Kubernetes environments.

Course Modules & Learning Outcomes

Metric Observability Foundations

Build a strong foundation in metrics-driven observability. Learn what metrics are, how Grafana supports observability, and how to use the Four Golden Signals to monitor latency, traffic, errors, and saturation. You'll also explore SLIs, SLOs, and SLAs and understand how they help measure and manage service reliability.

Prometheus & Grafana Fundamentals

Learn why Prometheus and Grafana are widely used for cloud-native observability and gain hands-on experience deploying them in Kubernetes. Explore the /metrics endpoint, understand counters, gauges, histograms, and summaries, learn PromQL fundamentals, and use the kube-prometheus-stack Helm chart to collect and query Kubernetes metrics.

Metric Instrumentation

Learn how to expose and collect meaningful application metrics. Explore in-code metric instrumentation, Prometheus metric instrumentation, and the fundamentals of auto-instrumentation with OpenTelemetry. You'll understand how to design metrics that provide useful operational insights.

Metric Design and Cardinality

Understand how metric cardinality affects the scalability and performance of Prometheus. Learn how to identify and diagnose high-cardinality metrics, avoid common metric design mistakes, and work with Prometheus native histograms for efficient metric collection and analysis.

Visualizing Metrics in Grafana

Transform metrics into actionable insights by building effective Grafana dashboards. Create time series panels, Golden Signal visualizations, histograms, heatmaps, KPI panels, state timelines, and saturation panels to monitor application performance, reliability, and resource utilization.

Alert Management

Learn the principles of effective alerting and understand how to create meaningful alerts using Grafana Alerting. Explore alerting philosophy and learn how to focus on actionable signals while reducing unnecessary alert noise.

Metrics Observability for AI Inference

Explore how metrics-driven observability applies to AI inference workloads. Learn about the key metrics and observability considerations involved in monitoring the performance and behavior of modern AI-powered applications.

Course Conclusion

Review the key concepts covered throughout the course and reinforce your understanding of metrics-driven observability using Prometheus and Grafana.

Course Features

• Hands-on exercises deploying and working with Prometheus and Grafana in Kubernetes environments.
• Practical experience collecting, instrumenting, querying, and analyzing metrics using Prometheus, PromQL, OpenTelemetry, and Grafana.
• Real-world dashboard creation covering the Four Golden Signals, KPIs, latency distributions, saturation, and application health.
• Practical guidance on scalable metric design, high-cardinality diagnosis, histograms, and effective alerting strategies.
• Modern observability concepts covering SLIs, SLOs, Kubernetes metrics, and metrics observability for AI inference workloads.

Who Should Enroll?

• DevOps and cloud engineers looking to build practical observability skills using Prometheus and Grafana.
• Site Reliability Engineers (SREs) responsible for monitoring service reliability and performance.
• Platform and Kubernetes engineers managing cloud-native applications and infrastructure.
• Developers interested in instrumenting applications and using metrics to understand application behavior and performance.
• Anyone looking to build practical skills in collecting, querying, visualizing, and acting on metrics in modern cloud-native environments.

Build the practical skills needed to instrument applications, collect and query metrics, create meaningful Grafana dashboards, and implement effective alerting using Prometheus and Grafana for metrics-driven observability.