Datadog — Prerequisites

What to Know Before Starting Datadog

Datadog is approachable for anyone who already understands observability concepts generally — it's largely a matter of learning where those concepts live in Datadog's UI and query syntax, not new fundamentals.

Required (Must Have)

1. Core Observability Concepts

Where to learn: This site's Prometheus and Grafana sections cover these concepts directly — Datadog packages the same ideas in a managed SaaS, so time spent there transfers almost entirely.

2. Container/Kubernetes Basics

Most real Datadog deployments run the Agent as a Kubernetes DaemonSet:

kubectl get pods -n monitoring    # Comfortable reading pod status
kubectl logs, kubectl describe    # Basic troubleshooting commands
helm install / helm upgrade       # Datadog Agent ships via Helm

You don't need deep Kubernetes expertise — just enough to deploy and inspect a Helm-installed workload.

3. Basic Application Instrumentation Awareness

Nice to Have (Speeds Up Learning)

Prior Prometheus/Grafana/ELK Experience

Overview's own framing applies directly here: if you already know Prometheus+Grafana+ELK, Datadog is largely a syntax translation exercise, not new conceptual ground. PromQL experience transfers almost directly to reading (though not writing identically) Datadog's query syntax.

A Programming Language for APM

To get real value from Module 03's APM instrumentation examples, comfort reading Python or Node.js helps — you don't need to write production code, just understand what tracer.wrap() or dd-trace's auto-patching is doing.

Terraform Basics (Optional)

Module 02 covers defining SLOs via Terraform's datadog_service_level_objective resource — if you've been through this site's Terraform section already, that module will feel immediately familiar rather than new syntax to learn cold.

What You Do NOT Need

Time Estimate

If you have the prerequisites above:

Start Here

Go to the Installation section to deploy the Datadog Agent, then proceed to Fundamentals.