observability

Instrument a Python service by integrating Prometheus for metrics collection, OpenTelemetry for tracing, and structured logging. This exposes production signals enabling reliable monitoring, incident response, and observability across microservices and cloud deployments.

Define SLOs and SLIs by implementing alerting rules and dashboards as code. This approach accelerates incident response and postmortems by providing end-to-end visibility into production-grade service reliability and performance.

Yes, you can use OpenTelemetry and Prometheus together. This Skill instruments applications to collect metrics with Prometheus and traces with OpenTelemetry, delivering comprehensive observability for production-grade Python services and data pipelines.

Create Grafana dashboards as code by applying sample configurations and code provided by the Skill. This validates observability best practices and enables structured visualization of metrics, traces, and logs across microservices and cloud deployments.

Yes, this Skill applies to production-grade Python services and data pipelines across cloud deployments. It enables Prometheus metrics collection, OpenTelemetry tracing, and structured logging to validate observability best practices.

Structured logging is needed to turn production signals into reliable insights. It accelerates incident response and postmortems by providing end-to-end visibility and searchable, contextualized log data across microservices.