Observability Stack — Grafana, Prometheus, Loki, Tempo
A three-day course that builds a complete LGTM observability stack (Loki, Grafana, Tempo, Prometheus/Mimir) from scratch for teams without an existing solution — correlating logs, metrics, and traces in a single dashboard, prepared for the first official Grafana certifications launching in 2026.
Four tools, one unified view
Observability rests on three pillars — logs, metrics, and traces — but their value only shows up once they are correlated in one place. The course builds a complete LGTM stack (Loki, Grafana, Tempo, Prometheus/Mimir) from scratch, so teams without an existing solution leave with a ready, production-grade configuration.
Market demand and upcoming certifications
Knowledge of the Grafana/Prometheus/Loki/Tempo stack now appears in thousands of job postings for DevOps and SRE engineers, and Grafana Labs is announcing its first official certifications for 2026. The course prepares participants both practically and for the exam material.
From installation to signal correlation
The final module focuses on what distinguishes a mature stack from a pile of separate tools — the exemplars mechanism linking metrics directly to traces, and consistent Grafana dashboards that let you go from a metric anomaly to a specific log and trace in a few clicks.
Benefits
- Deploy a complete LGTM stack (Loki, Grafana, Tempo, Prometheus/Mimir) from scratch
- Configure correlation of logs, metrics, and traces in a single Grafana dashboard
- Build dashboards and alerts ready for a production environment
- Prepare for the first official Grafana certifications launching in 2026
Who is this training for?
Prerequisites
- Basic knowledge of Kubernetes or cloud-native environments
- Familiarity with Linux basics and working with containers
Training program
LGTM stack architecture from the ground up
- The three pillars of observability: logs, metrics, traces — and why they need to be combined
- The role of each component: Prometheus/Mimir (metrics), Loki (logs), Tempo (traces), Grafana (visualization)
- Installing and configuring the stack in Kubernetes from scratch
- Cost model and scaling for the LGTM stack compared to SaaS solutions
Prometheus and Mimir — metrics in practice
- Collecting metrics: exporters, service discovery, scrape configs
- PromQL — the metrics query language and common patterns
- Mimir as long-term, scalable storage for metrics
- Alerting rules and Alertmanager integration
Loki and Tempo — logs and traces without scattered tools
- Loki: label-indexing architecture instead of full-text indexing
- LogQL — querying and filtering logs in practice
- Tempo: collecting distributed traces, OpenTelemetry integration
- Correlating trace IDs across logs, metrics, and traces
Grafana — dashboards, alerts, and signal correlation
- Building dashboards that combine data from Prometheus, Loki, and Tempo in a single view
- Exemplars — linking metrics directly to traces
- Alerting in Grafana and incident escalation
- Preparation for the first official Grafana certifications (2026)
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from Observability — OpenTelemetry, the Grafana Stack, and AIOps in our catalog?
The existing course assumes an observability stack is already running and focuses on advanced instrumentation (OpenTelemetry), AIOps, and predictive monitoring. This course is for teams that do not yet have a solution deployed — it teaches building the entire LGTM stack from scratch, step by step, with a focus on four specific tools (Loki, Grafana, Tempo, Prometheus/Mimir), rather than advanced automation on top of an already existing platform.
How is the LGTM stack different from Prometheus and Grafana — Kubernetes Monitoring in our catalog?
The Prometheus and Grafana course focuses on cloud-native metrics monitoring. This course extends the scope to full observability — logs (Loki) and distributed traces (Tempo) alongside metrics — and correlating all three signals in a single Grafana dashboard, which is key when diagnosing incidents in a microservices architecture.
Why the LGTM stack instead of a ready-made SaaS solution (Datadog, New Relic)?
The LGTM stack (open source, maintained by Grafana Labs) gives full control over costs at large scale and avoids vendor lock-in. The course presents the cost model for both approaches so participants can make an informed decision about when self-hosted LGTM pays off and when SaaS is the better operational choice.
Does the course prepare you for Grafana certification?
Yes — Grafana Labs is launching its first official certifications in 2026, and the final module maps the course material onto the announced exam scope, covering dashboard configuration, PromQL/LogQL queries, and alert management.
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