Observability with Grafana, Prometheus,Loki, Alloy and Tempo

• Fundamentals of Observability (types of telemetry data, metric collection methods etc.)
• Prometheus (Installation, Configuration and Usage) comprising 21 lectures.
• Installation of Grafana on Windows, Mac, Linux (multiple flavours) and with Docker.
• Architecture of Highly Available and Highly Scalable Grafana for Produciton use.
• Dashboard Design Best Practices (Browser Apps, Backend Apps and Infrastructure)
• Building Dashboards and Graphs in Grafana
• Creating and Managing Alerts and Notifications in Grafana
• Integration with MySQL, SQL Server, AWS CloudWatch, GCP etc.
• Grafana Loki: Retrieval and Visualisation of Logs
• Administration of Grafana (Users, Teams, OAuth integraiton, LDAP integration etc.)
• Opentelemetry
• Grafana Alloy
• Grafana Tempo

Master observability with the Grafana Stack, including Grafana Loki for logs, Grafana Tempo for distributed tracing, Grafana Alloy as an Open Telemetry (OTel) collector, and Grafana Mimir for large-scale enterprise metrics storage, by by enrolling in this top rated course, which has been the best selling Grafana course on Udemy for seven consecutive years!

This course provides a comprehensive, hands-on path to building modern observability systems. It starts with metrics using Prometheus and progresses to logs, traces, alerting, and custom dashboards in Grafana.

We begin with the core concepts of observability, telemetry data, and methods for metric collection. Then, you'll dive into Prometheus — learning how to install, configure, and use it like a pro.

Next, you'll deploy Grafana across Windows, macOS, Linux (including Ubuntu and Amazon Linux), and Docker. Once your stack runs, we cover Grafana dashboard design for real-world use cases: APIs, infrastructure, and microservices.

In the logging section, you'll work with Grafana Loki to ingest and visualise logs, including dynamic label extraction from unstructured logs.

Then we go deeper: you'll learn the fundamentals of OpenTelemetry and set up Grafana Alloy to receive, process, and export OTel metrics and traces. You'll instrument microservices (in Python and C#) and export signals to Grafana Tempo, where you'll trace distributed calls and analyze service graphs with TraceQL.

Now also included is Grafana Mimir, a highly scalable time-series database for storing metrics at scale. You'll learn what Mimir is, how it works, and how to deploy it locally in monolithic mode and microservices mode into Kubernetes.

To make it practical, the course is based on a fictional online retailer, ShoeHub, with mock data, dashboards, alerts, and services that simulate real-world observability use cases.

No setup headaches — you'll also get instant access to a browser-based playground powered by Killer Coda, so you can start experimenting without installing anything.

Included in the course:

• Docker Compose files for Prometheus, Grafana, Loki, Alloy, Tempo, Mimir, ShoHub metrics, and Example Microservices Tracing.

• Sample dashboards and panel configurations.

• Log generator script in Python.

• Set up guides for multiple platforms.

• Binary executable files for ShoeHub and the example microservices (if you don't want to use Docker).

• Optional cloud lab environment (Killer Coda) for instant hands-on practice.

I will respond promptly via the Udemy Q&A system if you encounter any issues or have questions.

Happy learning — and welcome to the world of observability!