Monitoring Course

Lesson 1 • Setting Monitoring Objectives

Guides students in defining measurable monitoring goals aligned with service requirements. Connects monitoring design to organizational outcomes.

Lesson 2 • What Monitoring Is and Why It Matters

Defines monitoring, its business value, and its role in operational reliability. Anchors all subsequent technical content in practical purpose.

Lesson 3 • Types of Monitoring Environments

Surveys infrastructure, application, network, and user-experience monitoring domains. Helps students map monitoring types to real-world contexts.

Lesson 4 • Monitoring Architecture Fundamentals

Explains data collection, transport, storage, and visualization layers. Students understand how components connect in a complete monitoring pipeline.

Lesson 1 • Metric Labeling and Cardinality

Explains how labels enrich metrics and how high cardinality degrades performance. Students design label schemas that balance detail with efficiency.

Lesson 2 • Metrics Pipeline Reliability

Addresses buffering, backpressure, and redundancy in metrics pipelines. Students build pipelines that survive component failures without data loss.

Lesson 3 • Instrumentation Strategies

Teaches code-level and agent-based instrumentation approaches for emitting metrics. Students choose the right strategy for each application type.

Lesson 4 • Metric Types and Data Models

Covers counters, gauges, histograms, and summaries with their appropriate use cases. Builds the conceptual model needed for accurate metric design.

Lesson 1 • Log Levels and Severity Classification

Defines standard severity levels and guidelines for their correct application. Proper classification reduces noise and speeds incident triage.

Lesson 2 • Structured vs. Unstructured Logging

Contrasts plain-text and structured log formats and their impact on searchability. Students adopt structured logging to enable efficient querying.

Lesson 3 • Log Retention, Archiving, and Compliance

Addresses retention schedules, archival tiers, and regulatory log-keeping requirements. Students balance storage costs with audit and compliance obligations.

Lesson 4 • Centralized Log Collection

Covers log shippers, aggregators, and ingestion pipelines for centralizing logs. Students design collection architectures that scale with log volume.

Lesson 1 • Trace Collection and Storage

Explains trace backends, ingestion pipelines, and storage considerations for traces. Students configure a trace collection stack for production use.

Lesson 2 • Distributed Systems and Observability Gaps

Explains why metrics and logs alone are insufficient for distributed systems. Motivates tracing as the missing pillar of full observability.

Lesson 3 • Analyzing Traces for Performance Issues

Teaches flame graphs, critical-path analysis, and error attribution using traces. Students pinpoint bottlenecks and cascading failures in service graphs.

Lesson 4 • Trace Anatomy and Propagation

Defines spans, trace IDs, parent-child relationships, and context propagation. Students understand how a trace is assembled across service boundaries.

Lesson 1 • Alert Fatigue and Noise Reduction

Diagnoses causes of alert fatigue and applies deduplication, grouping, and inhibition. Students reduce noise so critical alerts receive immediate attention.

Lesson 2 • Anomaly Detection Alerting

Introduces statistical and machine-learning-based anomaly detection for alerting. Students apply anomaly detection where fixed thresholds are impractical.

Lesson 3 • Threshold-Based Alerting

Covers static and dynamic threshold configuration for metric-based alerts. Students set thresholds that catch real problems without generating excessive noise.

Lesson 4 • On-Call Scheduling and Escalation

Covers on-call rotation design, escalation policies, and responder well-being. Students build sustainable on-call programs that balance coverage and burnout risk.

Lesson 1 • Executive and Business Dashboards

Translates technical metrics into business KPIs for leadership audiences. Students bridge the gap between engineering data and business decision-making.

Lesson 2 • Dashboard Design Principles

Applies visual hierarchy, information density, and audience-awareness to dashboard layout. Students avoid common design mistakes that obscure critical information.

Lesson 3 • Dashboard Governance and Maintenance

Establishes ownership, versioning, and review processes for dashboard lifecycle management. Students prevent dashboard sprawl and keep visualizations accurate over time.

Lesson 4 • Choosing the Right Chart Types

Maps data characteristics to appropriate chart types—time series, heatmaps, gauges, and tables. Students select visualizations that accurately represent underlying data.

Lesson 1 • Serverless and Function Monitoring

Addresses cold starts, invocation metrics, and error tracking for serverless functions. Students gain visibility into workloads with no persistent infrastructure.

Lesson 2 • Cloud Cost and Resource Efficiency Monitoring

Monitors cloud spend, resource utilization, and waste to optimize infrastructure costs. Students connect performance data to financial accountability.

Lesson 3 • Cloud-Native Monitoring Challenges

Identifies unique challenges of monitoring ephemeral, auto-scaled cloud resources. Students adapt traditional monitoring strategies to cloud-native environments.

Lesson 4 • Container and Orchestration Monitoring

Covers metrics, logs, and traces specific to containerized workloads and orchestrators. Students monitor container health, resource usage, and scheduling behavior.

Lesson 1 • Monitoring Maturity Models

Introduces maturity frameworks to assess and advance an organization's monitoring capability. Students benchmark current state and plan targeted improvements.

Lesson 2 • Service-Level Objectives and Error Budgets

Formalizes SLOs, SLIs, and error budgets as the foundation of reliability management. Students use error budgets to balance feature velocity and reliability.

Lesson 3 • Building a Monitoring Culture

Promotes shared ownership of monitoring across development, operations, and product teams. Students drive adoption of monitoring practices organization-wide.

Lesson 4 • Post-Incident Review and Monitoring Improvement

Uses post-incident analysis to identify monitoring gaps and drive iterative improvements. Students close the feedback loop between incidents and monitoring design.