Grafana — Prerequisites
What You Need Before Starting
Grafana is a visualization and alerting layer that sits on top of data sources you need to already understand — the tool itself is genuinely quick to learn, but it can't compensate for not knowing what the underlying metrics/logs actually mean or how to query them.
1. Basic PromQL fluency (strongly recommended — this is the real prerequisite, not Grafana's UI)
Most of this course's Grafana material assumes you can already write basic PromQL (rate(), sum by, histogram_quantile()) — Grafana's panels are just a UI wrapped around whatever query you write, and it can't turn a query you don't know how to write into a useful dashboard. If Prometheus/PromQL is entirely new, this platform's dedicated Prometheus/monitoring-fundamentals material (if covering that technology) is worth a pass first — trying to learn Grafana's dashboard-building UI and PromQL simultaneously means neither builds as cleanly as learning them in sequence.
2. Basic understanding of what metrics, logs, and traces each are (recommended)
You don't need deep expertise in each, but understanding conceptually that metrics are numeric time-series data (a request count, a latency value), logs are discrete text records of events, and traces follow a single request's path across multiple services, will make this course's Loki (log) and Tempo (trace) sections click faster — Grafana visualizes all three, but they're genuinely different kinds of data requiring different query languages and different mental models.
3. Basic Kubernetes familiarity (helpful, not required)
A meaningful share of this course's dashboard examples reference Kubernetes concepts (pods, namespaces, kube-state-metrics) since that's an extremely common real-world deployment target for Grafana. If Kubernetes is unfamiliar, the examples will still mostly make sense at a surface level, but a basic pass over this platform's Kubernetes-fundamentals material first will help the Kubernetes-specific dashboard examples land as concrete rather than abstract.
4. Comfort with YAML and basic command-line usage (required)
Grafana's provisioning system (data sources, dashboards, alert rules as code) is YAML-based, and this course's material assumes you can read and lightly modify YAML without it being new syntax to decode. Basic Docker familiarity is also assumed for the installation examples (docker run, docker-compose up) — if these are unfamiliar, a quick pass over basic Docker usage first will remove friction from the Installation section specifically.
5. What you do *not* need yet
Quick self-check
Given the raw metric http_requests_total (a counter), can you say what rate(http_requests_total[5m]) actually computes, and why you can't just use the raw counter value directly on a dashboard panel to show "current request rate"? If yes, you're ready for this course's Fundamentals section — that's the exact kind of PromQL fluency the rest of this material assumes. If that's not immediately clear, spend more time on basic PromQL first; this course teaches Grafana's dashboarding features on top of that foundation, not instead of it.