Observability Engineering with Metrics, Logs, and Traces Training in Kigali, Rwanda by EduCore Global Skills

Module 1: Observability Fundamentals

Core competencies for Observability Fundamentals.

• Monitoring versus observability: known unknowns and unknown unknowns
• The three pillars (metrics, logs, traces) and the role of events and profiles
• Observability in distributed systems, microservices, and cloud-native environments
• Key concepts: cardinality, dimensionality, and context
• Building an observability culture and aligning with DevOps and SRE practices

Module 2: Metrics Collection and Instrumentation

Core competencies for Metrics Collection and Instrumentation.

• Metric types: counters, gauges, histograms, and summaries
• Instrumenting applications and infrastructure (Prometheus client libraries, StatsD)
• The RED, USE, and Four Golden Signals methods
• Naming conventions, labels, and managing cardinality
• Metric pipelines, scraping versus push models, and long-term storage (Prometheus, Thanos, Mimir)

Module 3: Logging Strategy and Log Management

Core competencies for Logging Strategy and Log Management.

• Structured logging, log levels, and consistent schemas
• Application, system, audit, and access logs
• Log collection and shipping (Fluent Bit, Fluentd, Logstash, Vector)
• Centralized storage and search (Elasticsearch, OpenSearch, Loki)
• Log retention, sampling, cost control, and sensitive data redaction

Module 4: Distributed Tracing

Core competencies for Distributed Tracing.

• Traces, spans, context, and trace propagation
• Instrumenting services for tracing (manual versus auto-instrumentation)
• Context propagation across HTTP, messaging, and async workflows
• Sampling strategies: head-based, tail-based, and adaptive
• Trace backends and analysis (Jaeger, Tempo, Zipkin) for latency and dependency debugging

Module 5: OpenTelemetry and Vendor-Neutral Instrumentation

Core competencies for OpenTelemetry and Vendor-Neutral Instrumentation.

• OpenTelemetry architecture: APIs, SDKs, and the Collector
• Instrumenting with OTel across languages (Java, Python, Go, Node.js)
• Collector pipelines: receivers, processors, and exporters
• Semantic conventions and resource attributes
• Migrating from proprietary agents to OpenTelemetry

Module 6: Correlating Signals and Visualization

Core competencies for Correlating Signals and Visualization.

• Linking metrics, logs, and traces with trace IDs and exemplars
• Building dashboards in Grafana that tell a story
• Service maps and dependency visualization
• Dashboard design principles and avoiding dashboard sprawl
• Exploratory debugging workflows: from symptom to root cause

Module 7: SLIs, SLOs, and Alerting

Core competencies for SLIs, SLOs, and Alerting.

• Defining SLIs, SLOs, SLAs, and error budgets
• Choosing meaningful indicators for user-facing reliability
• Alerting best practices: symptom-based, actionable, and low-noise
• Burn rate alerts and multi-window alerting strategies
• On-call practices, escalation, and reducing alert fatigue

Module 8: Observability for Cloud-Native and Kubernetes Environments

Core competencies for Observability for Cloud-Native and Kubernetes Environments.

• Monitoring Kubernetes clusters, nodes, pods, and workloads
• Service mesh observability (Istio, Linkerd) and eBPF-based telemetry
• Observing serverless and event-driven architectures
• Database, queue, and third-party dependency monitoring
• Infrastructure as code for observability configuration

Module 9: Incident Response and Root Cause Analysis

Core competencies for Incident Response and Root Cause Analysis.

• Using telemetry for incident detection, triage, and diagnosis
• Debugging high-latency, error-rate, and saturation issues
• Continuous profiling and performance analysis
• Blameless postmortems and learning from incidents
• Chaos engineering and testing observability readiness

Module 10: Scaling, Cost, and Observability Maturity

Core competencies for Scaling, Cost, and Observability Maturity.

• Managing telemetry volume, storage costs, and retention tiers
• Designing scalable, highly available observability platforms
• Build versus buy: open-source stack versus commercial platforms (Datadog, New Relic, Honeycomb, Grafana Cloud)
• Security, access control, and compliance for telemetry data
• Observability maturity model and a roadmap for adoption

How is Observability Engineering with Metrics, Logs, and Traces Training delivered?

Observability Engineering with Metrics, Logs, and Traces Training is delivered through instructor-led classroom, live online, private in-house corporate training depending on the selected cohort or organizational arrangement.

What are the fees for Observability Engineering with Metrics, Logs, and Traces Training?

The published course investment is KES 80,000 and USD 1,800. Location-specific fees are shown where configured.

How long is Observability Engineering with Metrics, Logs, and Traces Training?

Observability Engineering with Metrics, Logs, and Traces Training runs for 5 Days unless a customized corporate schedule is agreed.

Do participants receive a certificate for Observability Engineering with Metrics, Logs, and Traces Training?

Participants who complete the course receive a Certificate of Completion from EduCore Global Skills.

Where can I take Observability Engineering with Metrics, Logs, and Traces Training?

Observability Engineering with Metrics, Logs, and Traces Training is available in Kigali, Rwanda. Current dates are shown only where real sessions have been configured.

Who should attend Observability Engineering with Metrics, Logs, and Traces Training?

Observability Engineering with Metrics, Logs, and Traces Training is designed for ICT Officers, IT Managers, Systems Administrators, Cybersecurity Teams, Digital Transformation Leads, Professionals and organizations that need practical capability in Cloud, DevOps, and Platform Engineering.

What are the prerequisites for Observability Engineering with Metrics, Logs, and Traces Training?

No mandatory prerequisite is required unless stated in the course outline. Practical workplace experience in the subject area is helpful.