Datadog Performance Monitoring Course

Lesson 1 • Installing the Datadog Agent

Covers agent installation on Linux, Windows, and containers. Connects platform architecture knowledge to a live, data-sending agent.

Lesson 2 • Core Observability Concepts

Defines metrics, logs, and traces as the three pillars of observability. Establishes vocabulary used throughout the course.

Lesson 3 • Datadog Platform Architecture Overview

Maps Datadog's major product areas and how they interconnect. Provides context for every feature introduced in later chapters.

Lesson 1 • Host and Process Monitoring

Uses the Infrastructure List and Host Map to monitor system-level resource usage. Provides the baseline visibility needed before container and cloud monitoring.

Lesson 2 • Cloud Infrastructure Integrations

Connects AWS, Azure, and GCP accounts to pull cloud-native metrics and events. Unifies cloud and on-premises visibility in a single monitoring plane.

Lesson 3 • Container and Kubernetes Monitoring

Configures the agent for Kubernetes clusters, pods, and container metrics. Extends host monitoring skills to dynamic, orchestrated environments.

Lesson 4 • Network Performance Monitoring

Enables NPM to visualise traffic flows between services and infrastructure components. Adds network-layer visibility to complement host and application monitoring.

Lesson 1 • Metric Queries and Functions

Explores the Datadog query language, aggregation functions, and arithmetic operations. Enables precise, multi-metric expressions for advanced visualisations.

Lesson 2 • Custom Metrics and DogStatsD

Teaches submitting application-level custom metrics via DogStatsD and the API. Extends monitoring beyond infrastructure into business-critical application events.

Lesson 3 • Understanding Metric Types

Explains gauges, counters, histograms, and distributions with real examples. Correct metric type selection directly affects dashboard accuracy.

Lesson 4 • Collecting Metrics with Integrations

Demonstrates enabling out-of-the-box integrations for common infrastructure components. Reduces manual instrumentation effort for standard technology stacks.

Lesson 1 • Log Collection Configuration

Sets up log collection from files, containers, and cloud services via the agent. Establishes the data pipeline that feeds all subsequent log analysis work.

Lesson 2 • Log Search and Exploration

Covers the Log Explorer query syntax, facets, and saved views. Enables rapid triage of incidents using structured and unstructured log data.

Lesson 3 • Log-Based Metrics and Analytics

Generates cost-efficient metrics from high-volume logs and builds log analytics dashboards. Bridges log data with the metrics layer for unified monitoring.

Lesson 1 • APM Concepts and Trace Anatomy

Defines spans, traces, and service maps within the context of distributed systems. Provides the conceptual foundation required before any instrumentation work.

Lesson 2 • Service Performance Monitoring

Covers service-level objectives, error budgets, and the Service Catalog. Elevates trace data into service reliability management for engineering teams.

Lesson 3 • Instrumenting Applications

Demonstrates auto-instrumentation and manual tracing for major language runtimes. Connects agent configuration knowledge to application-level telemetry.

Lesson 1 • Notification and Escalation Routing

Configures notification channels, message templates, and escalation policies. Ensures the right person receives actionable context at the right time.

Lesson 2 • Advanced Alerting Conditions

Teaches anomaly detection, outlier detection, and forecast monitors. Moves alerting beyond static thresholds to adaptive, ML-assisted detection.

Lesson 3 • Monitor Types and Configuration

Covers metric, log, APM, and composite monitors with their configuration options. Selecting the right monitor type is the first step in effective alerting.

Lesson 1 • Synthetic API and Browser Tests

Creates API tests and browser-based synthetic checks for critical user flows. Provides continuous availability validation independent of real user traffic.

Lesson 2 • RUM Analytics and Session Replay

Analyses Core Web Vitals, error rates, and session replays to diagnose UX issues. Translates raw user session data into prioritised frontend improvements.

Lesson 3 • Real User Monitoring Setup

Instruments web and mobile applications with the RUM SDK to capture user sessions. Connects frontend experience data to backend APM traces for end-to-end visibility.

Lesson 1 • Observability as Code

Manages monitors, dashboards, and SLOs as version-controlled infrastructure using Terraform and the Datadog API. Enables repeatable, auditable observability configuration.

Lesson 2 • Cloud Security Monitoring

Configures Cloud SIEM detection rules and Cloud Security Posture Management. Extends observability into security threat detection and compliance validation.

Lesson 3 • Incident Management Integration

Uses Datadog Incident Management to declare, track, and retrospect on incidents. Closes the loop between detection, response, and continuous improvement.

Lesson 4 • Service Level Objectives

Defines, tracks, and reports on SLOs using metric and monitor-based targets. Aligns engineering reliability work with business availability commitments.