The Master in Observability Engineering program is designed for DevOps engineers, SREs, and platform engineers who need to build comprehensive observability stacks for cloud-native applications. Modern distributed systems generate enormous volumes of telemetry — this program teaches you how to instrument, collect, correlate, and act on metrics, logs, and traces using the industry's leading open-source tools. From OpenTelemetry auto-instrumentation to eBPF-based kernel-level observability and continuous profiling with Parca and Pyroscope, this course covers the full spectrum of modern observability engineering.
The Master in Observability Engineering certification validates expertise across all three pillars of observability — metrics (Prometheus and Alertmanager), logs (Loki and ELK Stack), and distributed traces (Jaeger and Grafana Tempo) — unified under the OpenTelemetry standard. Participants learn to instrument applications in multiple languages using OTel SDKs and auto-instrumentation, build Grafana dashboards from raw telemetry data, define and track SLOs with error budget dashboards, correlate signals across pillars for faster root cause analysis, and apply advanced observability techniques including eBPF-powered network observability (Pixie and Hubble) and continuous CPU/memory profiling (Parca and Pyroscope).
This program is designed for SREs, DevOps engineers, platform engineers, and backend developers who are responsible for system reliability, performance, and incident response in cloud-native environments. It equally benefits observability leads at organizations migrating from traditional monitoring to full-stack observability, and architects designing telemetry pipelines for microservices at scale. Professionals targeting roles such as Observability Engineer, SRE, or Platform Engineer will find this certification essential.
Participants build a complete observability stack for a 5-service e-commerce application running on Kubernetes. The project covers: instrumenting all services with OpenTelemetry SDKs, deploying Prometheus and Grafana with USE/RED dashboards, implementing distributed tracing with Grafana Tempo and correlating traces with Loki logs, defining 3 SLOs with error budget dashboards and multi-burn-rate alerts, and applying Pixie for zero-instrumentation network-level observability. The complete stack is deployed using Helm charts and reviewed by a senior SRE.
Graduates receive an observability engineering interview preparation kit containing 140+ Q&A covering PromQL query design, Alertmanager configuration scenarios, distributed tracing architecture decisions, SLO calculation problems, LogQL queries, and eBPF observability use cases. Questions are drawn from real SRE and observability engineer interviews at top-tier cloud companies and technology firms.
It is a certification that validates mastery of all three pillars of observability — metrics, logs, and distributed traces — using industry-standard tools including OpenTelemetry, Prometheus, Grafana, Jaeger, Loki, and advanced eBPF-based techniques.
Monitoring asks predefined questions about known failure modes — dashboards and alerts on expected metrics. Observability allows you to ask new questions about your system's behavior using rich telemetry data (metrics + logs + traces) without deploying new code. This course teaches both, with emphasis on observability engineering for distributed systems.
The course covers OpenTelemetry (SDKs and Collector), Prometheus, Alertmanager, Grafana, Jaeger, Grafana Tempo, Loki, Promtail, Elasticsearch, Kibana, Pixie, Hubble/Cilium, Parca, and Pyroscope — 12+ tools in total, all applied in live labs.
Basic Kubernetes and Linux knowledge is sufficient. Experience with Prometheus or Grafana is helpful but not required. The course is designed to take participants from observability fundamentals to advanced eBPF and continuous profiling techniques progressively.
OpenTelemetry (OTel) is a CNCF project providing a vendor-neutral standard for collecting metrics, logs, and traces from applications. It replaces proprietary SDKs, allowing you to instrument once and export to any backend (Prometheus, Jaeger, Tempo, Datadog, etc.). This course covers OTel instrumentation in depth for multiple languages.
SLOs (Service Level Objectives) define the reliability targets for your service. Observability provides the telemetry data — SLI metrics from Prometheus, error rate traces, and latency distributions — that powers SLO dashboards and multi-burn-rate alerting. A dedicated module covers SLO specification, error budget calculation, and Grafana dashboard implementation.
eBPF allows programs to run in the Linux kernel to capture system calls, network flows, and application behavior without code changes. Tools like Pixie and Hubble use eBPF to provide deep Kubernetes observability — HTTP traces, pod-level metrics, and network policies — with zero application instrumentation, ideal for legacy services or third-party workloads.
The exam includes scenario-based questions covering observability architecture decisions, PromQL query writing, Alertmanager configuration, SLO specification and error budget calculations, distributed tracing sampling strategies, and tool selection for different observability challenges across a microservices environment.
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