Prometheus and Grafana Course

Lesson 1 • Introduction to Prometheus and Grafana

Positions Prometheus as a metrics engine and Grafana as a visualisation layer. Sets expectations for the full tool stack covered in the course.

Lesson 2 • Why Service Monitoring Matters

Establishes the business and technical case for monitoring. Connects reliability goals to concrete monitoring requirements.

Lesson 3 • Observability Pillars Explained

Defines metrics, logs, and traces as complementary signals. Clarifies how each pillar addresses different failure scenarios.

Lesson 1 • Installation Methods and Prerequisites

Covers binary, Docker, and package-manager installation paths. Students select the method appropriate for their environment.

Lesson 2 • Verifying a Running Prometheus Instance

Uses the built-in UI and API to confirm targets are up and data is flowing. Establishes a baseline health check habit.

Lesson 3 • Core Configuration File Structure

Explains prometheus.yml syntax: global, scrape_configs, and rule_files blocks. Correct configuration is the prerequisite for all subsequent labs.

Lesson 4 • Prometheus Architecture Deep Dive

Maps internal components: retrieval, TSDB, HTTP API, and alerting. Understanding internals prevents misconfiguration in later steps.

Lesson 1 • Blackbox Exporter for Synthetic Probing

Uses Blackbox Exporter to probe HTTP, TCP, DNS, and ICMP endpoints. Provides external availability perspective independent of internal metrics.

Lesson 2 • Exporter Concepts and Taxonomy

Defines what an exporter is and how it exposes the /metrics endpoint. Classifies exporters by target type to guide selection.

Lesson 3 • Application-Level Instrumentation

Integrates Prometheus client libraries into application code. Students expose custom business and performance metrics.

Lesson 4 • Database and Middleware Exporters

Configures exporters for relational databases, caches, and message brokers. Extends visibility beyond host-level data.

Lesson 1 • PromQL Data Types and Selectors

Introduces instant vectors, range vectors, scalars, and strings. Correct type usage prevents query errors in all downstream work.

Lesson 2 • Rate, Increase, and Delta Functions

Converts counters into per-second rates and computes changes over time. These functions are the foundation of throughput and error-rate queries.

Lesson 3 • Histogram and Summary Metrics

Queries latency distributions using histogram_quantile and summary quantiles. Enables SLO-aligned percentile calculations.

Lesson 4 • Advanced PromQL Techniques

Combines binary operators, vector matching, and subqueries for complex analysis. Prepares students for production-grade alerting expressions.

Lesson 1 • Routing, Grouping, and Inhibition

Configures route trees to direct alerts to the right receivers. Grouping and inhibition reduce noise during large-scale incidents.

Lesson 2 • Alert Quality and Runbook Design

Applies SLO-based thresholds and multi-window burn-rate alerts. Runbook annotations guide on-call engineers to fast resolution.

Lesson 3 • Alerting Rule Fundamentals

Defines alert rules using PromQL expressions, for durations, and labels. Rules are the trigger mechanism for the entire alerting pipeline.

Lesson 4 • Notification Receivers and Templates

Integrates email, Slack, PagerDuty, and webhook receivers. Custom templates make notifications informative and actionable.

Lesson 1 • Dashboard and Panel Fundamentals

Creates dashboards with time-series, stat, gauge, and table panels. Panel types are matched to the nature of each metric.

Lesson 2 • Variables and Template Queries

Adds dashboard variables backed by label_values queries for dynamic filtering. Variables transform static dashboards into reusable fleet-wide tools.

Lesson 3 • Dashboard Best Practices and Governance

Applies naming conventions, layout principles, and version control for dashboards. Governance prevents dashboard sprawl in team environments.

Lesson 4 • Annotations, Links, and Drill-Downs

Overlays deployment events as annotations and links panels to related dashboards. Context enrichment accelerates root-cause analysis.

Lesson 1 • Relabelling for Label Normalisation

Uses relabel_configs to rename, drop, and transform labels before ingestion. Consistent labels are required for accurate aggregation and alerting.

Lesson 2 • File-Based and DNS Service Discovery

Implements file_sd_configs and dns_sd_configs for lightweight automation. These methods work without cloud provider APIs.

Lesson 3 • Metric Relabelling and Cardinality Control

Applies metric_relabel_configs to drop high-cardinality series at scrape time. Cardinality control protects TSDB performance and storage.

Lesson 4 • Static vs. Dynamic Service Discovery

Contrasts static_configs with automated discovery mechanisms. Dynamic discovery is essential for containerised and cloud-native workloads.

Lesson 1 • Performance Tuning and Capacity Planning

Profiles Prometheus resource usage and forecasts storage growth. Capacity planning prevents outages caused by monitoring infrastructure itself.

Lesson 2 • Federation and Hierarchical Scraping

Configures Prometheus federation to aggregate metrics across clusters. Federation enables global views without centralising all raw data.

Lesson 3 • Remote Storage Integrations

Connects Prometheus to long-term storage backends via remote_write and remote_read. Extends retention beyond local TSDB limits.

Lesson 4 • Security Hardening and Access Control

Enables TLS, basic auth, and Grafana role-based access control. Security hardening is mandatory before exposing monitoring to production networks.