Datadog — From Zero to Hero

Datadog is a unified observability SaaS platform: a lightweight Agent that runs next to your infrastructure and application, plus a family of products built on the telemetry it (and your instrumented code) sends — Infrastructure Monitoring, Log Management, APM (distributed tracing), Monitors & SLOs, and a Software Catalog that maps every one of those signals back to a specific service and its owner. This course is written for developers and SREs — the people who configure the Agent, write the tagging, tune the sampling, and get paged when a monitor fires.

Two facts many existing tutorials get wrong or skip entirely, because Datadog’s model here has more layers than it first appears: log ingestion and log indexing are two separate steps — you can send every log to a low-cost Archive while only a filtered subset is actually indexed and billed for search, and you can still generate metrics from logs that were never indexed at all. And “trace sampling” is really two independent controls — ingestion-time sampling (how many spans reach Datadog) and server-side retention filters (which ingested traces stay searchable) — with trace-derived metrics unaffected by either one. This course teaches both pipelines accurately and distinctly, along with the tagging convention — unified service tagging — that ties metrics, logs, and traces back to one service.

What this course covers

Module	You will learn
Foundations	What Datadog is, the Agent and integrations, unified service tagging, a tour of the UI
Infrastructure & Metrics	Agent-collected metrics, custom metrics via DogStatsD, Metrics without Limits, dashboards
Log Management	Collection and processing pipelines, indexes vs. exclusion filters, log-based metrics, archives
APM & Distributed Tracing	Traces, spans and the service map, ingestion vs. retention sampling, trace metrics, profiling
Correlating Logs, Traces & Metrics	Trace-log injection, unified service tagging in practice, the Service Catalog, RUM context
Monitors, Alerting & SLOs	Monitor types and thresholds, composite monitors, SLO error budgets and burn rates, Synthetics
Production & Ecosystem	Datadog as code with Terraform, cost governance, security basics, a production rollout checklist

How to read it

Each lesson opens with the idea in one sentence, shows real Agent config, DogStatsD/API calls, and CLI or Terraform snippets, has a diagram of the moving parts, and ends with a one-line takeaway plus a short quiz. Work top to bottom — later modules assume the tagging, pipeline, and sampling vocabulary from these first lessons.

🐶 The one-line takeaway

Datadog is the Agent, plus a family of products built on the same tagged telemetry — metrics, logs, and traces are separate pipelines, each with its own ingestion, retention, and cost controls, and unified service tagging (env, service, version) is what lets one deployable unit’s data stay correlated across all three. This course teaches each pipeline’s real mechanics and the correlation model that connects them, starting from the Agent itself.