Course · Training · Workshop
Monitoring and Observability with Prometheus and Grafana
Learn how to practically monitor, analyze, and visualize applications and systems with Prometheus and Grafana — including alerting, SLOs, and Kubernetes best practices.
A two-day intensive course focusing on monitoring and observability of applications with Prometheus and visualizing metrics with Grafana. Participants will learn the installation, configuration, and effective use of Prometheus for monitoring applications and creating meaningful dashboards with Grafana in a Kubernetes environment — to ensure stability and performance. We cover **service discovery, recording rules, PromQL, alerting (Alertmanager & Grafana Alerting), SLO/SLA tracking, histograms/exemplars**, plus **HA & long-term storage** (Thanos/Cortex/Mimir) and **security/costs/retention**.
What participants say
Great training with exercises that were immediately relevant to my daily work. The training environment was easy to set up and felt almost like our prod environment!
Application Developer
The course "Prometheus and PromQL" by letsboot.ch met my expectations. The teacher, Jan-Hendrik Heuing, impressed with his expertise and competence. I can wholeheartedly recommend this course and letsboot.ch.
Leiter IT Entwicklung Platform & Services
These customers booked courses in the same topic cluster.More customers →
Content
Prometheus is a powerful open-source monitoring and alerting system purpose-built for modern, distributed, and containerized applications. In this course, we show how to use Prometheus with Grafana effectively to monitor application health, identify performance issues, and ensure software quality.
Course topics (hands-on focus):
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Intro & Architecture
- Data model (labels/series), pull model, TSDB
- Components: Prometheus, exporters, Alertmanager, Pushgateway (when to use)
- Deployment options: standalone, Prometheus Operator, kube-prometheus-stack (Helm)
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Install & Configure
- Prometheus on Kubernetes (Helm/Operator) and Docker/Compose
- Service discovery (Kubernetes, EC2, Consul) and relabeling patterns
- Scrape config, jobs/targets, multi-cluster/namespace layouts
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Instrumentation & Exporters
- App instrumentation best practices (counter/gauge/histogram/summary)
- Histograms & exemplars for latency and trace correlation
- Key exporters: node_exporter, kube-state-metrics, cAdvisor, blackbox_exporter, DB exporters
- OpenTelemetry bridge (OTel Collector → Prometheus)
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PromQL & Recording Rules
- Query basics, label matching, joins
- rate/irate, histogram quantiles, Apdex/latency buckets
- Recording rules & groups for performance and reuse
- SLO/SLA metrics: error budget, availability & latency
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Grafana Dashboards
- Data source config, time ranges, transformations
- Dashboard design, panels, variables, library panels
- Exemplars in Grafana, drill-downs, annotations
- Best practices for SRE, infra & app monitoring
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Alerting
- Prometheus alert rules, templating, severity design
- Alertmanager: routing, inhibition, silence, receivers
- Grafana Alerting: when to use; harmonizing with Alertmanager
- Runbooks & annotations: from alert to action
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Operations, Scale & Reliability
- Retention & TSDB tuning, WAL/compaction, capacity
- High availability: sharding/HA pairs, Thanos/Cortex/Mimir for LTS & global query
- Federation vs. remote write/read, multi-cluster strategies
- Self-monitoring; watchdog alerts
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Security & Compliance
- TLS, authN/Z (reverse proxy, OAuth proxy), network scoping
- Multi-tenancy (Mimir/Cortex), tenant isolation via labels/namespaces
- PII/Compliance: what not to put into metrics
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Cost Control & Cardinality
- Detecting label explosion, cardinality checks
- Metric hygiene: naming, labeling, intervals, downsampling/recording
- Storage cost vs. resolution vs. retention: guardrails
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Troubleshooting & Patterns
- Debugging slow queries, PromQL optimization
- Exporter/target issues, scrape errors, stale series
- Incident dashboards (Golden Signals, RED/USE)
Hands-on labs (examples):
- Lab 1: Helm deploy (kube-prometheus-stack), access & security
- Lab 2: Service discovery & relabeling — scrape only what matters
- Lab 3: PromQL drills (rates, histograms, joins, quantiles)
- Lab 4: Recording rules for SLOs + SLI dashboards in Grafana
- Lab 5: Building Grafana dashboards from scratch (panel design, variables, alerts)
- Lab 6: Instrumenting application metrics (counter, gauge, histogram in sample app)
- Lab 7: Alerting setup (rules + Alertmanager routing), runbook linking
- Lab 8: Blackbox checks (HTTP/TCP/ICMP) + incident dashboard
- Lab 9: Retention/cardinality tuning, self-monitoring & watchdog
- Lab 10: Thanos for long-term storage & HA querying
Scenarios and hands-on labs are based on Kubernetes and containerized applications.
The actual course content may differ from the above depending on the trainer, delivery, duration and the composition of participants.
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More about Prometheus & Grafana
Prometheus uses a dimensional data model (labels) and pull-based scraping with built-in **service discovery**. **PromQL** enables flexible queries, **recording rules** speed up common metrics, and **Alertmanager** handles notifications. **Grafana** provides visualization, supports **exemplars**, and SRE-style dashboards. For **long-term storage & HA**, systems like **Thanos/Cortex/Mimir** are commonly used.Further resources:
History
Prometheus started at SoundCloud in 2012 and joined the CNCF in 2016 as the second project after Kubernetes. In combination with Grafana, it has become the de facto standard for metrics-based monitoring and SRE-led observability. The ecosystem (Operator, Thanos/Mimir/Cortex, OpenTelemetry integration) keeps evolving.