Modern Observability: From Logs to Insights

Outline

• Introduction to Observability: Definition, importance, key concepts (metrics, logs, traces), and an overview of the observability stack.
• Metrics with Prometheus: Introduction, setting up Prometheus, writing PromQL queries, and best practices for metrics collection.
• Visualization with Grafana: Introduction to Grafana, integrating with Prometheus, creating dashboards and alerts, and advanced visualization techniques.
• Log Aggregation with Loki: Overview of Loki, integration with Prometheus and Grafana, setting up log collection pipelines, and querying logs with LogQL.
• Centralized Logging with ELK: Introduction to ELK (Elasticsearch, Logstash, Kibana), setting up ELK for centralized logging, ingesting and parsing logs, and visualizing logs with Kibana.
• Anomaly Detection and Alerting: Importance of anomaly detection, setting up anomaly detection with Prometheus and Grafana, configuring alerts, and real-world use cases.
• Distributed Tracing: Introduction to distributed tracing, setting up Jaeger for tracing, integrating tracing with microservices, and analyzing trace data.
• Audit Logging and Security Monitoring: Introduction to audit logging, setting up and configuring audit logs, integrating with ELK and Loki, and monitoring security events.
• Deployment, Redeployment, and Rollback Based on Monitoring Data: Using monitoring data in CI/CD pipelines, automated rollbacks, and case studies on deployment failures and recovery.
• Advanced Log Aggregation Techniques: Scaling log aggregation, best practices for log retention, log enrichment, and troubleshooting with aggregated logs.
• Integrating Observability with Cloud-Native Architectures: Observability in Kubernetes, using Prometheus and Grafana in Kubernetes, sidecar patterns, and monitoring serverless architectures.
• Case Studies and Hands-On Workshop: Real-world observability implementations, setting up a full observability stack (Prometheus, Grafana, Loki, ELK, Jaeger), group discussions, and Q&A.