Monitoring and Observability for DevOps
Follow this page to learn Monitoring and Observability for DevOps - core skills for ensuring reliable, high-performing systems in cloud-native environments.
Understand technical tools and concepts such as metrics collection (Prometheus, CloudWatch), distributed tracing (Jaeger, OpenTelemetry), log aggregation (ELK/EFK stack), and real-time alerting to diagnose, visualize, and resolve issues before they impact users.
Important Concepts in Monitoring and Observability for DevOps
Core Concepts of Monitoring & Observability
✦ Monitoring vs. Observability
✦ The "Three Pillars" of Observability :
Metrics , Logs , Traces
✦ Importance of SLA, SLO, SLI in observability culture
✦ Golden Signals (from SRE) :
Latency, Traffic, Errors , Saturation
Metrics & Time-Series Monitoring
✦ What are time-series metrics ? Used to plot CPU, memory, disk usage, etc.
✦ Popular Metric Tools:
- Prometheus: Pull-based, label-based storage, rule-based alerting
- InfluxDB, Graphite
✦ Use cases
✦ Instrumenting custom code using libraries like prometheus_client in Python
Logs and Log Aggregation
✦ Understanding log types :
Application logs , System logs (journald, syslog) , Access logs (e.g., Nginx, Apache)
✦ Log levels :
DEBUG, INFO, WARNING, ERROR, CRITICAL
✦ Tools for log collection & storage:
ELK Stack , Fluentd / Fluent Bit , Loki (Grafana)
✦ Log centralization and real-world automation with Fluentd + Elasticsearch
Distributed Tracing
✦ What is tracing ?
✦ Popular tools :
- Jaeger
- OpenTelemetry
- Zipkin
✦ Trace span, parent/child relationships
Example : Identifying latency in a multi-tier application
Alerting Systems
✦ Alerting strategy:
Avoid alert fatigue , Prioritize severity , Use rate-limiting and deduplication
✦ Popular tools :
Prometheus Alertmanager , Grafana alerts , PagerDuty
✦ Use cases :
- Send Slack/email alerts for high error rates
- Auto-ticket generation on disk threshold breach
Visualization Dashboards
✦ Tools:
- Grafana: most widely used
- Kibana: Elasticsearch data visualization
- Datadog, Splunk, New Relic
✦ Best practices:
- Use templated dashboards
- Apply color-coded thresholds
- Provide filters (time range, services)
✦ Dashboard use cases:
- Nginx error rate by domain
- Kubernetes pod performance over time
Monitoring in Kubernetes
✦ Key metrics :
Pod restarts , CPU/memory usage , Node status, job completions
✦ Tools :
- kube-state-metrics, metrics-server
- Prometheus Operator
- Kube-prometheus-stack
✦ Kubernetes events: watch for CrashLoopBackOff, image pull errors, etc.
✦ Use case: Grafana + Loki + Promtail to monitor container logs
Cloud-Native Monitoring
✦ AWS:
- CloudWatch Metrics, Logs, Alarms, Dashboards
- X-Ray for tracing
- Custom metrics via CloudWatch Agent or SDK
✦ Azure:
- Azure Monitor, Log Analytics, Application Insights
✦ Automate alerts for EC2 CPU > 90%, or S3 bucket event triggers
Monitoring Automation & CI/CD Integration
✦ Integrate Prometheus/Grafana dashboards deployment into Helm/K8s manifests
✦ CI/CD checks:
- Validate service availability before deploy
- Rollback on failed health checks
✦ GitOps for managing alerting rules and dashboard configs
Real-World DevOps Scenarios
✦ Scenario 1: Monitoring a high-traffic web app
- Use Prometheus + Grafana to plot HTTP status codes
- Setup alert for spike in 5xx errors
✦ Scenario 2: Log aggregation for compliance
- Stream logs via Fluent Bit to S3/ES
- Index logs with structured formats for audit queries
✦ Scenario 3: Observability in microservices
- Trace requests using Jaeger
- Visualize service dependencies and bottlenecks
✦ Scenario 4: Slack alert integration
- Integrate Alertmanager with Slack webhook for production alerts
Monitoring and Observability for DevOps in 2025
Understand how to build robust, high-performing cloud-native systems with focus on monitoring and observability for DevOps best practices.
Why Monitoring and Observability for DevOps Matter
For any modern DevOps team, monitoring and observability for DevOps are very critical. Effective strategies provide not just system health metrics, but deep insights to diagnose problems fast, enforce SLAs, and accelerate incident response across hybrid and multi-cloud environments.
Monitoring vs. Observability
Monitoring is the process of collecting and analyzing predefined metrics and logs to detect known issues in a system. It answers the question: “Is the system working as expected ?” Tools like Prometheus, Grafana, and ELK Stack are commonly used to track CPU usage, memory consumption, error rates, and uptime. Monitoring is mostly reactive as it alerts you when something goes wrong, based on thresholds or conditions set .
Observability on the other hand is a broader concept. It refers to the system's ability to help engineers understand why something is wrong. It gives deep insights into a system's internal state by analyzing three core pillars: metrics, logs, and traces.
In short, monitoring tells you that something is wrong, while observability helps you understand why it's wrong.
Logs vs Metrics vs Distributed Tracing in DevOps
- Logs capture detailed event data (errors, warnings, requests).
- Metrics offer quantifiable measures (CPU, latency, error rates) for ongoing health.
- Distributed tracing reveals how requests move across services, ideal for microservices and cloud-native troubleshooting.
Understand the difference between logs vs metrics vs distributed tracing in DevOps , as it gives you complete visibility into systems behavior.
Top DevOps Observability Tools 2025: Comparison and Features
Understand the top DevOps observability tools 2025 - from open-source to enterprise solutions :
| Use Case | Open Source | Cloud/Commercial | Key Strengths |
|---|---|---|---|
| Metrics & Alerting | Prometheus | AWS CloudWatch, Datadog | Real-time metrics, alerting, service monitoring |
| Visualization & Dashboards | Grafana | New Relic, Dynatrace | Centralized dashboards, customizable panels |
| Log Aggregation (ELK Stack) | Elasticsearch, Logstash, Kibana (ELK/EFK) | Splunk, Sumo Logic | Log search, analytics, retention |
| Distributed Tracing | Jaeger, OpenTelemetry | Lightstep, AppDynamics | End-to-end tracing, bottleneck detection |
| Incident & Alert Management | Alertmanager | PagerDuty, OpsGenie | Automated escalation, runbooks, cross-tool integration |
DevOps Metrics Collection with Prometheus or CloudWatch : Step-by-Step
- Install Prometheus or leverage AWS CloudWatch agents for data collection.
- Export metrics such as CPU, memory, HTTP requests, and custom business KPIs.
- Integrate with Grafana for centralized dashboards, visualizing system health and SLO/SLA performance.
- Define proactive alerts within Prometheus/CloudWatch and connect to Slack, email, or PagerDuty.
- Review and act on anomalous trends or SLO breaches using real-time data and historical analysis.
How to Set Up Real-Time Alerting in Cloud-Native Environments
- Configure alert rules in Prometheus Alertmanager, CloudWatch Alarms, or Datadog Monitors.
- Use multi-channel notifications: Slack, email, SMS, instant messaging.
- Implement auto-remediation scripts triggered by critical alerts for rapid response.
- Regularly tune and suppress noise to prevent alert fatigue and maximize signal relevance.
ELK Stack for Log Aggregation in Microservices
Utilize the ELK stack for log aggregation in microservices :
- Send container and service logs to Logstash or Fluentd.
- Store logs in Elasticsearch for searching and analytics.
- Visualize and analyze logs with Kibana, correlating application and infrastructure events for root-cause analysis.
Distributed Tracing with Jaeger and OpenTelemetry
Achieve end-to-end visibility across your microservices architecture :
- Instrument code with OpenTelemetry libraries; export spans to Jaeger.
- View service maps and trace graphs in Jaeger’s dashboard.
- Quickly diagnose bottlenecks, latency issues, and failure points.
AIOps Predictive Monitoring: The Future of DevOps Observability
- Deploy AIOps features in modern platforms (e.g., Datadog, New Relic) for predictive monitoring.
- Use machine learning for anomaly detection, incident prediction, and automated root cause analysis.
- Integrate with CI/CD for shift-left observability, catching issues earlier in the development lifecycle.
- Enable collaboration for Dev, Ops, and SRE teams with shared, actionable insights.
Frequently Asked Questions
Find quick answers to common questions about monitoring and observability
- Centralize collection of logs, metrics, and traces.
- Implement distributed tracing with tools like Jaeger and OpenTelemetry.
- Set up actionable, relevant real-time alerting.
- Utilize AIOps predictive monitoring for proactive incident handling.
- Prometheus, Grafana, AWS CloudWatch, ELK stack, Jaeger, OpenTelemetry, Datadog, Splunk.
- Install exporters/agents, define custom and infrastructure metrics, visualize with dashboards, set SLI/SLO alerts.
- Provides centralized, scalable log storage and analytics, essential for troubleshooting distributed systems.
- Reveals the flow of requests through services; vital for debugging latency, bottlenecks, and inter-service issues.