Visão Geral
O curso Datadog Observability for Kubernetes tem como objetivo capacitar profissionais na implementação de práticas de observabilidade em ambientes Kubernetes utilizando a plataforma Datadog. O treinamento aborda monitoramento de clusters, métricas, logs, traces, segurança e performance de aplicações containerizadas, proporcionando visibilidade completa e melhoria na confiabilidade dos sistemas.
Conteúdo Programatico
Module 1 – Introduction to Kubernetes Observability
- Observability concepts for containerized environments
- Kubernetes architecture overview
- Challenges in monitoring Kubernetes
- Datadog integration with Kubernetes
- Key observability pillars (metrics, logs, traces)
Module 2 – Datadog Setup for Kubernetes
- Installing Datadog Agent in Kubernetes
- Helm deployment
- Configuration of integrations
- Auto-discovery features
- Security and RBAC considerations
Module 3 – Kubernetes Metrics Monitoring
- Cluster-level metrics
- Node and pod metrics
- Resource utilization (CPU, memory, disk)
- Kubernetes state metrics
- Metrics tagging and filtering
Module 4 – Log Collection and Analysis
- Collecting container logs
- Log processing pipelines
- Filtering and searching logs
- Correlating logs with Kubernetes resources
- Troubleshooting with logs
Module 5 – Distributed Tracing in Kubernetes
- Introduction to APM in Kubernetes
- Instrumenting microservices
- Service maps and dependencies
- Trace analysis
- Performance bottleneck detection
Module 6 – Dashboards and Visualization
- Kubernetes dashboards in Datadog
- Custom dashboards
- Visualization of cluster health
- Service-level dashboards
- Sharing and collaboration
Module 7 – Alerting and Incident Management
- Kubernetes-specific alerts
- SLO-based alerting
- Anomaly detection
- Incident response workflows
- Alert optimization
Module 8 – Optimization and Best Practices
- Performance tuning for Kubernetes workloads
- Cost optimization strategies
- Scaling monitoring
- Security considerations
- Best practices for observability in Kubernetes