Observability often grows one tool at a time. Logs may live in Elasticsearch, metrics in Prometheus or InfluxDB, and traces may be missing completely. Each tool has its own query language, storage model, and dashboards. As a result, finding the cause of a slow request can take longer than it should.

This talk explains how to connect logs, metrics, and traces with the Grafana LGTM stack: Loki, Grafana, Tempo, and Mimir. It also shows how OpenTelemetry and Grafana Alloy can collect and connect tObservability often grows one tool at a time. Logs may live in Elasticsearch, metrics in Prometheus or InfluxDB, and traces may be missing completely. Each tool has its own query language, storage model, and dashboards. As a result, finding the cause of a slow request can take longer than it should.

This talk explains how to connect logs, metrics, and traces with the Grafana LGTM stack: Loki, Grafana, Tempo, and Mimir. It also shows how OpenTelemetry and Grafana Alloy can collect and connect telemetry data from applications.

Ned will share lessons from building and running this stack in a production environment. The platform is self-hosted on AWS, runs in Docker, and is protected by Nginx. The talk will cover how the main parts fit together, how data moves through the system, and how to connect an alert to the related logs and trace.

Attendees will leave with a practical view of the architecture, the main setup steps, and the tradeoffs to consider when building a self-hosted observability platform.lemety data from applications.

Ndshare lessons from uilding and running this stack in a production nvironment.he pltform is sef-hosted on AWS, runs in Docer,an is protcted by Nginx. The talk will cover how the main part fit together, how data moves through the system, and how to onnect an alert to the elated logs and trace.

Attendees wll leave with a raccal view of the architecture, the main setup steps, and the tradeoffs to csider when building a self-hosted observability platform.