Linux Observability Mastery
@amitmund
July 09, 2026
Linux Observability Mastery
The Complete Beginner to Advanced Guide to Linux Observability, Monitoring, Logging, Tracing, Profiling, Performance Analysis, eBPF, OpenTelemetry, Production Debugging, SRE Practices, and Enterprise Observability Platforms
Course Goal
This course is designed to take you from absolute beginner to production-ready Linux Observability Engineer, SRE, Platform Engineer, DevOps Engineer, Infrastructure Engineer, Performance Engineer, or Distinguished Systems Engineer.
By the end of this learning track, you will be able to:
- Understand the three pillars of observability
- Monitor Linux systems like Google, Meta, Netflix, and Amazon
- Debug production issues quickly using logs, metrics, traces, and profiling
- Master Linux performance analysis
- Use Prometheus, Grafana, Loki, Tempo, OpenTelemetry, and eBPF
- Build complete observability platforms
- Design highly observable distributed systems
- Prepare for SRE, DevOps, Platform Engineering, and System Design interviews
Prerequisites
- Linux Mastery
- Linux Networking Mastery
- Bash Scripting
- Docker (Recommended)
- Kubernetes (Recommended)
- Basic System Administration
Course Structure
Module 1 — Observability Fundamentals
Chapter 1 — Introduction to Observability
- What is Observability?
- Monitoring vs Observability
- Three Pillars of Observability
- Telemetry
- Production Use Cases
- Observability Maturity Model
Chapter 2 — Observability Architecture
- Data Collection
- Storage
- Aggregation
- Query Layer
- Visualization
- Alerting
- Incident Response
Chapter 3 — Linux Metrics
- Host Metrics
- Kernel Metrics
- Process Metrics
- Service Metrics
- Custom Metrics
Chapter 4 — Linux Logging
- System Logs
- Application Logs
- Structured Logging
- Log Rotation
- Log Retention
Chapter 5 — Distributed Tracing
- Trace Fundamentals
- Span
- Context Propagation
- Correlation IDs
- End-to-End Tracing
Module 2 — Linux Metrics
Chapter 6 — CPU Monitoring
Chapter 7 — Memory Monitoring
Chapter 8 — Disk Monitoring
Chapter 9 — Filesystem Monitoring
Chapter 10 — Network Monitoring
Chapter 11 — Process Monitoring
Chapter 12 — Kernel Metrics
Chapter 13 — NUMA Metrics
Module 3 — Linux Logging
Chapter 14 — syslog
Chapter 15 — journald
Chapter 16 — rsyslog
Chapter 17 — Structured Logging
Chapter 18 — Log Rotation
Chapter 19 — Centralized Logging
Chapter 20 — Log Retention
Module 4 — Performance Monitoring
Chapter 21 — Load Average
Chapter 22 — CPU Scheduling
Chapter 23 — Memory Pressure
Chapter 24 — I/O Performance
Chapter 25 — Disk Latency
Chapter 26 — Network Latency
Chapter 27 — System Bottlenecks
Module 5 — Linux Performance Tools
Chapter 28 — top
Chapter 29 — htop
Chapter 30 — vmstat
Chapter 31 — iostat
Chapter 32 — mpstat
Chapter 33 — sar
Chapter 34 — pidstat
Chapter 35 — free
Chapter 36 — uptime
Chapter 37 — dstat
Chapter 38 — iotop
Chapter 39 — iftop
Chapter 40 — nethogs
Module 6 — Network Observability
Chapter 41 — ss
Chapter 42 — netstat
Chapter 43 — tcpdump
Chapter 44 — tshark
Chapter 45 — Wireshark
Chapter 46 — NetFlow
Chapter 47 — sFlow
Chapter 48 — Packet Analysis
Module 7 — eBPF Observability
Chapter 49 — Introduction to eBPF
Chapter 50 — BPF Compiler Collection (BCC)
Chapter 51 — bpftrace
Chapter 52 — Kernel Instrumentation
Chapter 53 — Dynamic Tracing
Chapter 54 — XDP
Chapter 55 — Performance Profiling
Module 8 — Profiling
Chapter 56 — perf
Chapter 57 — Flame Graphs
Chapter 58 — CPU Profiling
Chapter 59 — Memory Profiling
Chapter 60 — I/O Profiling
Chapter 61 — Lock Contention
Module 9 — Prometheus
Chapter 62 — Architecture
Chapter 63 — Prometheus Server
Chapter 64 — Node Exporter
Chapter 65 — Exporters
Chapter 66 — PromQL
Chapter 67 — Recording Rules
Chapter 68 — Alert Rules
Module 10 — Grafana
Chapter 69 — Dashboards
Chapter 70 — Panels
Chapter 71 — Variables
Chapter 72 — Alerting
Chapter 73 — Dashboard Design
Module 11 — Loki
Chapter 74 — Loki Architecture
Chapter 75 — Promtail
Chapter 76 — LogQL
Chapter 77 — Log Aggregation
Chapter 78 — Log Correlation
Module 12 — Tempo & Jaeger
Chapter 79 — Tempo
Chapter 80 — Jaeger
Chapter 81 — Trace Storage
Chapter 82 — Trace Visualization
Chapter 83 — Sampling
Module 13 — OpenTelemetry
Chapter 84 — OpenTelemetry Architecture
Chapter 85 — Metrics
Chapter 86 — Logs
Chapter 87 — Traces
Chapter 88 — Context Propagation
Chapter 89 — Instrumentation
Chapter 90 — Collector
Module 14 — Alerting
Chapter 91 — Alertmanager
Chapter 92 — Alert Routing
Chapter 93 — Notification Channels
Chapter 94 — Escalation Policies
Chapter 95 — SLO Alerts
Module 15 — SRE Observability
Chapter 96 — SLI
Chapter 97 — SLO
Chapter 98 — SLA
Chapter 99 — Error Budgets
Chapter 100 — Incident Response
Module 16 — Docker Observability
Chapter 101 — Docker Metrics
Chapter 102 — Container Logs
Chapter 103 — Container Tracing
Chapter 104 — cAdvisor
Module 17 — Kubernetes Observability
Chapter 105 — kube-state-metrics
Chapter 106 — Metrics Server
Chapter 107 — CAdvisor
Chapter 108 — Cluster Monitoring
Chapter 109 — Pod Monitoring
Chapter 110 — Node Monitoring
Module 18 — Cloud Observability
Chapter 111 — AWS CloudWatch
Chapter 112 — Azure Monitor
Chapter 113 — Google Cloud Operations
Chapter 114 — Hybrid Cloud Monitoring
Module 19 — Security Observability
Chapter 115 — auditd
Chapter 116 — Falco
Chapter 117 — OSQuery
Chapter 118 — SIEM
Chapter 119 — Security Dashboards
Module 20 — Capacity Planning
Chapter 120 — Resource Forecasting
Chapter 121 — Growth Modeling
Chapter 122 — Capacity Dashboards
Chapter 123 — Scaling Metrics
Module 21 — Troubleshooting
Chapter 124 — CPU Bottlenecks
Chapter 125 — Memory Leaks
Chapter 126 — Disk Issues
Chapter 127 — Network Issues
Chapter 128 — Kernel Debugging
Chapter 129 — Production Incidents
Module 22 — Enterprise Observability
Chapter 130 — Multi-Cluster Monitoring
Chapter 131 — Multi-Region Monitoring
Chapter 132 — High Availability
Chapter 133 — Long-Term Storage
Chapter 134 — Observability Pipelines
Module 23 — Performance Engineering
Chapter 135 — Benchmarking
Chapter 136 — Load Testing
Chapter 137 — Stress Testing
Chapter 138 — Capacity Testing
Chapter 139 — Optimization
Module 24 — Interview Preparation
Chapter 140 — Linux Observability Questions
Chapter 141 — Prometheus Questions
Chapter 142 — Grafana Questions
Chapter 143 — OpenTelemetry Questions
Chapter 144 — SRE Interview Questions
Chapter 145 — Mock Interviews
Module 25 — Bonus
Chapter 146 — Best Practices
Chapter 147 — Common Mistakes
Chapter 148 — Troubleshooting Checklist
Chapter 149 — Observability Cheat Sheet
Chapter 150 — Future of Observability
Commands Covered
System Monitoring
- top
- htop
- vmstat
- iostat
- mpstat
- pidstat
- sar
- free
- uptime
- dstat
Network Monitoring
- ss
- netstat
- tcpdump
- tshark
- iftop
- nethogs
Profiling
- perf
- bpftrace
- bpftool
- strace
- ltrace
Logging
- journalctl
- logger
- rsyslog
- logrotate
Technologies Covered
Monitoring
- Prometheus
- Node Exporter
- Alertmanager
Visualization
- Grafana
Logging
- Loki
- Promtail
- rsyslog
- journald
Tracing
- OpenTelemetry
- Tempo
- Jaeger
- Zipkin
Profiling
- perf
- eBPF
- BCC
- bpftrace
- Flame Graphs
Cloud
- AWS CloudWatch
- Azure Monitor
- Google Cloud Operations Suite
Every Chapter Includes
Every chapter follows the same professional learning structure:
- Learning Objectives
- Theory
- Internal Working
- Linux Kernel Internals
- Metrics Flow
- Log Pipeline
- Trace Lifecycle
- Mermaid Diagrams
- ASCII Diagrams
- Architecture Diagrams
- Sequence Diagrams
- Commands
- Configuration Examples
- Bash Scripts
- Python Examples
- Docker Examples
- Kubernetes Examples
- Cloud Examples
- Production Architectures
- Performance Optimization
- Troubleshooting Guide
- Common Mistakes
- Security Notes
- Best Practices
- Hands-on Labs
- Mini Projects
- Capstone Projects
- Exercises
- Quiz
- Interview Questions
- Cheat Sheet
- Summary
- References
- Further Reading
Hands-on Labs
- Install Prometheus & Node Exporter
- Create Linux Monitoring Dashboards
- Configure Grafana Alerts
- Centralize Logs with Loki
- Instrument Applications with OpenTelemetry
- Analyze CPU Bottlenecks with perf
- Trace Kernel Events using eBPF
- Build Distributed Tracing with Tempo
- Monitor Docker Containers
- Observe Kubernetes Clusters
- Build a Complete Monitoring Stack
- Investigate a Production Incident
- Create SLO Dashboards
- Build Capacity Planning Dashboards
- Design an Enterprise Observability Platform
Capstone Projects
- Enterprise Linux Monitoring Platform
- Kubernetes Observability Platform
- Production Incident Response Dashboard
- Centralized Logging Platform
- Distributed Tracing Platform
- eBPF Performance Analysis Toolkit
- Enterprise SRE Dashboard
- Cloud-Native Observability Stack
- Capacity Planning System
- Complete OpenTelemetry Observability Platform
Research & Documentation
Study and analyze:
- Linux Kernel Documentation
- Prometheus Documentation
- Grafana Documentation
- Loki Documentation
- OpenTelemetry Specification
- eBPF Documentation
- perf Documentation
- Brendan Gregg's Performance Resources
- Google SRE Book
- OpenTelemetry Semantic Conventions
Estimated Course Size
- 25 Modules
- 150 Chapters
- 6,500+ Pages
- 3,000+ Command & Configuration Examples
- 1,500+ Architecture & Flow Diagrams
- 500+ Hands-on Labs
- 150+ Production Case Studies
- Complete Linux Observability, Monitoring, SRE & Performance Engineering Interview Preparation
Final Outcome
After completing this learning track, you will be able to:
- Build and operate enterprise-grade Linux observability platforms.
- Monitor, troubleshoot, and optimize Linux systems using metrics, logs, traces, and profiling.
- Deploy and manage Prometheus, Grafana, Loki, Tempo, OpenTelemetry, and eBPF in production.
- Diagnose complex performance issues across Linux, containers, Kubernetes, and cloud environments.
- Design scalable observability architectures for modern distributed systems.
- Confidently perform as a Linux Observability Engineer, SRE, Platform Engineer, DevOps Engineer, Performance Engineer, or Distinguished Infrastructure Engineer.
- Successfully prepare for advanced observability, SRE, Linux, cloud, and system design interviews.