OpenTelemetry
Master OpenTelemetry for unified observability and instrumentation of modern cloud-native applications.Preview OpenTelemetry course
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- Start with Core Concepts – Understand observability basics, OpenTelemetry architecture, and its components.
- Learn Hands-On Instrumentation – Instrument applications in multiple languages (Python, Java, Node.js, Go) using OTel SDKs and auto-instrumentation.
- Configure Pipelines – Use the OpenTelemetry Collector to receive, process, and export telemetry to popular backends.
- Work on Real-World Projects – Implement distributed tracing in microservices, create dashboards for metrics, and analyze logs for debugging.
- Focus on Multi-Environment Usage – Deploy OTel in Kubernetes, integrate with CI/CD pipelines, and monitor serverless applications.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By completing this course, you will:
- Understand OpenTelemetry architecture and its components (SDK, Collector, Exporters).
- Instrument applications manually and with auto-instrumentation libraries.
- Configure OpenTelemetry Collector for data pipelines.
- Implement distributed tracing in microservices and serverless environments.
- Capture application metrics for performance monitoring.
- Configure logging with OpenTelemetry for unified observability.
- Export telemetry to tools like Jaeger, Prometheus, Grafana, and Datadog.
- Deploy OpenTelemetry in Kubernetes and cloud-native environments.
- Optimize telemetry pipelines for scale and performance.
- Troubleshoot and fine-tune observability in production systems.
- What is Observability?
- Evolution from Monitoring to Observability
- OpenTelemetry Overview
- Key components: SDKs, APIs, Collector, Exporters
- Data types: Traces, Metrics, Logs
- Manual instrumentation
- Auto-instrumentation for supported languages
- Distributed tracing concepts
- Setting up traces for microservices
- Context propagation
- Capturing performance metrics
- Exporting to Prometheus & Grafana
- Unified logs integration
- Correlating logs with traces
- Collector architecture and pipelines
- Processors, receivers, and exporters
- Exporting telemetry to Jaeger, Zipkin
- Connecting to commercial APM tools
- OTel in containerized workloads
- Service mesh integration (Istio/Linkerd)
- AWS Lambda, Azure Functions, GCP Functions
- Instrumenting CI/CD pipelines
- Observability-as-code
- Sampling strategies for traces
- High-performance telemetry pipelines
- Microservices observability with tracing and metrics
- Full-stack telemetry integration (frontend + backend)
- Cloud-native observability dashboard
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Scaling OpenTelemetry in production
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Common pitfalls and troubleshooting
Upon completion, learners will receive an Industry-Recognized Certificate of Completion from Uplatz in OpenTelemetry. This certificate validates expertise in observability, tracing, metrics, and logs collection for cloud-native applications. It signals proficiency in designing end-to-end observability pipelines and integrating OTel into enterprise-grade DevOps environments. Certification holders can confidently showcase skills to employers seeking observability engineers, SREs, and DevOps specialists in cutting-edge technology domains.
- Observability Engineer
- Site Reliability Engineer (SRE)
- DevOps Engineer
- Cloud Monitoring Specialist
- Instrumentation Engineer
- What is OpenTelemetry and why is it important?
OpenTelemetry is an open-source framework for standardized telemetry collection (traces, metrics, logs) in distributed systems, crucial for unified observability. - How does OpenTelemetry differ from traditional monitoring tools?
Unlike tool-specific agents, OpenTelemetry offers vendor-neutral APIs and SDKs, enabling flexible backend integration and consistent instrumentation. - What are the core components of OpenTelemetry?
SDK/APIs (instrumentation), Collector (pipeline), and Exporters (integration with backend tools). - Explain the role of the OpenTelemetry Collector.
The Collector receives telemetry data, processes it (e.g., filtering, batching), and exports it to analysis backends. - How does distributed tracing work in OpenTelemetry?
It tracks requests across services, propagating context (trace IDs) to correlate spans and visualize latency bottlenecks. - How do you instrument a Python or Java application with OpenTelemetry?
Install language SDKs, add instrumentation libraries, configure exporters, and initialize tracing in code or via auto-instrumentation. - How are metrics collected and exported in OTel?
Metrics APIs capture data points, which are processed by the Collector and sent to tools like Prometheus or Grafana. - What is context propagation and why is it important?
Context propagation ensures trace identifiers flow across services, allowing correlation of spans in distributed systems. - How do you deploy OpenTelemetry in Kubernetes?
Use Helm charts or manifests to deploy the Collector as DaemonSet or sidecar and configure pipelines for workloads. - What are common challenges with OpenTelemetry?
Challenges include handling telemetry volume, sampling traces efficiently, and managing integration complexity across environments.