Monitoring, Observability & Search

Observability Fundamentals & Three Pillars

Foundation + Concepts

Overview

Establish comprehensive understanding of modern observability principles built on the three pillars: logs, metrics, and traces. Learn how these data types work together to provide complete visibility into system behavior, performance, and health. Master the distinction between monitoring and observability, understand telemetry data collection strategies, and develop skills in designing observability architectures that scale with system complexity.

Learning Resources

Course Title	Provider	Description	Level	Action
The Three Pillars of Observability	O'Reilly	Understanding the core data types—Logs, Metrics, and Traces—that form the basis of modern observability	Beginner	Read Guide
The Three Pillars of Observability	Fastly	Explains what logs, metrics, and traces are, their pros and cons, and how they provide insight into system behavior	Beginner	Learn More
SRE Principles	Google SRE	Learning the core principles of Site Reliability Engineering focusing on using software engineering to automate operations	Intermediate	Study SRE
Distributed Tracing	Jaeger	Learning how distributed tracing works to track a single request's journey across multiple microservices	Intermediate	Learn Tracing
Tracing Made Easy: A Beginner's Guide to Jaeger	OpenObserve	Beginner-friendly tutorial on setting up Jaeger to monitor and visualize request flows in distributed systems	Beginner	Tutorial
Defining SLIs, SLOs, and SLAs	YouTube	Learning the critical SRE concepts of Service Level Indicators, Objectives, and Agreements to measure reliability	Intermediate	Watch Explanation

Hands-On Activities

Observability Strategy Design: Define observability requirements and data collection strategy for your Task Manager
SLI/SLO Definition: Establish service level indicators and objectives for key application metrics
Telemetry Implementation: Instrument your application to collect logs, metrics, and traces
Distributed Tracing Setup: Implement request tracing across your microservices architecture

Elastic Stack & Centralized Logging

Hands-On + Tools

Overview

Master the Elastic Stack (Elasticsearch, Logstash, Kibana, and Beats) for implementing centralized logging, search, and analytics. Learn to design scalable log aggregation architectures, create powerful search queries, and build comprehensive dashboards for operational insights. Develop skills in log parsing, data enrichment, and creating real-time analytics that provide actionable intelligence for system operations and business decisions.

Learning Resources

Course Title	Provider	Description	Level	Action
Elastic Stack (ELK) Getting Started	Elastic	Official documentation and tutorials on getting started with Elastic Observability and setting up the stack	Beginner	Get Started
Centralized Logging with ELK Stack	Medium	Step-by-step guides on setting up Elasticsearch, Logstash, and Kibana for centralized log aggregation	Intermediate	Read Guide
Set Up Centralized Logging System with ELK	HostMyCode	Practical tutorial on configuring Logstash pipelines to ingest data and preparing the environment for log analysis	Intermediate	Follow Tutorial
Search Implementation Guide	Elastic	Practical guide to implementing website search using Elasticsearch, from data ingestion to UI integration	Intermediate	Implementation Guide
Deploy and Manage Monitoring Agents	Microsoft/Google	Learning to deploy agents on infrastructure to collect logs, metrics, and traces for centralized analysis	Intermediate	Azure Agent Video
Elasticsearch Integration with Google Cloud	Google Cloud	Learning how to connect and integrate Elasticsearch with Google Cloud services for enhanced data analysis	Intermediate	Integration Guide

Hands-On Activities

ELK Stack Setup: Deploy complete Elasticsearch, Logstash, Kibana stack for your Task Manager
Log Pipeline Configuration: Configure Logstash pipelines to parse and enrich application logs
Search Implementation: Build search functionality for your application using Elasticsearch
Data Visualization: Create comprehensive Kibana dashboards for operational insights

Metrics & Monitoring with Prometheus/Grafana

Metrics + Visualization

Overview

Master metrics collection and visualization using the Prometheus and Grafana ecosystem. Learn to design effective monitoring strategies, configure metric exporters, and create actionable dashboards that provide real-time insights into system performance. Develop skills in query languages (PromQL), alerting rules, and building monitoring solutions that scale from single applications to complex distributed systems.

Learning Resources

Course Title	Provider	Description	Level	Action
Prometheus & Grafana Full Course	YouTube	Beginner-friendly tutorials on setting up Prometheus with exporters and connecting it to Grafana for visualization	Beginner	Watch Course
Explore Prometheus with Easy Hello World Projects	Grafana	Hands-on beginner project to get started with Prometheus and Grafana for metrics collection and visualization	Beginner	Start Project
Create Monitoring Dashboards	Elastic	Build visual dashboards to monitor key metrics, logs, and system health in real-time using Kibana visualizations	Intermediate	Dashboard Guide
Google Cloud Operations Suite	Google Cloud	Google Cloud's native suite of tools for monitoring, logging, tracing, and diagnostics for GCP applications	Intermediate	GCP Monitoring
Cloud Operations Hands-on with gcloud	Google Codelabs	Hands-on codelab for creating custom dashboards and configuring log-based alerts using gcloud CLI	Intermediate	Start Codelab
Effective Monitoring Dashboards	DAI	Best practices for creating dashboards that effectively communicate key information to different audiences	Intermediate	Dashboard Tips

Hands-On Activities

Prometheus Setup: Deploy Prometheus with custom exporters for your Task Manager application
Grafana Dashboard Creation: Build comprehensive monitoring dashboards with key performance indicators
Custom Metrics Implementation: Instrument your application with business and technical metrics
Alert Rules Configuration: Set up intelligent alerting based on metric thresholds and anomalies

Advanced Observability & AI-Powered Analytics

AI + Advanced Analytics

Overview

Explore cutting-edge observability techniques using AI and machine learning for predictive monitoring and intelligent analytics. Learn to implement anomaly detection, automated root cause analysis, and predictive maintenance systems. Develop skills in leveraging AI for pattern recognition, trend analysis, and proactive incident prevention while building observability solutions that evolve from reactive to predictive operations.

Learning Resources

Course Title	Provider	Description	Level	Action
AI-Powered Log Analysis & Anomaly Detection	Elastic	Using machine learning to automatically analyze logs, categorize events, and detect anomalies without manual rules	Advanced	ML Tutorial
BigQuery Anomaly Detection Overview	Google Cloud	Overview of Google Cloud's BigQuery for anomaly detection using supervised and unsupervised models	Advanced	BQ Anomaly Detection
AI for Predictive Monitoring	IBM	Exploring how AI is moving observability from reactive to predictive using machine learning to forecast issues	Advanced	IBM Insights
AI Observability Knowledge Base	Dynatrace	Using predictive and causal AI to analyze telemetry data and monitor AI-specific metrics like token usage	Advanced	Dynatrace Guide
AI-Powered Monitoring Platforms	AIM Technologies	Overview of modern platforms that use AI for predictive analytics, automated anomaly detection, and event correlation	Intermediate	Platforms Overview
Alert Fatigue & Noise Reduction	Netdata	Understanding causes of alert fatigue and strategies to reduce noise by creating smarter, more meaningful alerts	Intermediate	Prevent Alert Fatigue

Hands-On Activities

ML-Based Anomaly Detection: Implement machine learning models for automated anomaly detection
Predictive Monitoring Setup: Build predictive models to forecast system performance issues
Intelligent Alerting System: Create AI-powered alerting with noise reduction and smart grouping
Automated Root Cause Analysis: Develop systems for automated incident correlation and analysis

Incident Management & SRE Practices

Operations + Best Practices

Overview

Master comprehensive incident management processes and Site Reliability Engineering practices. Learn to design effective on-call rotations, create actionable runbooks, and conduct blameless post-mortems that drive continuous improvement. Develop skills in incident response coordination, escalation procedures, and building resilient systems while establishing SLA/SLO frameworks and implementing chaos engineering practices.

Learning Resources

Course Title	Provider	Description	Level	Action
Incident Management Process	Atlassian	Structured process for responding to unplanned service interruptions: Identify, Log, Categorize, Prioritize, and Respond	Intermediate	Learn Process
Blameless Post-mortem Process	Google SRE	Adopting a culture of learning from failure by conducting post-mortems that focus on systemic causes	Intermediate	SRE Post-mortems
Post-mortem Templates	Atlassian	Templates and guides for analyzing incidents to identify root causes and create effective preventative action items	Intermediate	Get Templates
Alert Runbooks	Atlassian	Creating runbooks with step-by-step instructions for operations teams to resolve system alerts consistently	Intermediate	Runbook Template
Runbook Best Practices and Examples	DrDroid	Templates and best practices for writing effective runbooks including troubleshooting tips and verification steps	Intermediate	Best Practices
On-Call Best Practices	PagerDuty	Setting up and managing effective on-call rotations to ensure 24/7 coverage while preventing team burnout	Intermediate	On-Call Guide

Hands-On Activities

Incident Response Plan: Develop comprehensive incident response procedures and escalation matrix
Runbook Creation: Create detailed operational runbooks for common incidents and maintenance tasks
Post-Mortem Process: Establish blameless post-mortem culture and documentation processes
On-Call Setup: Design and implement effective on-call rotation and alerting systems

OpenTelemetry & Advanced Tracing

Standards + Implementation

Overview

Master OpenTelemetry as the industry standard for cloud-native observability instrumentation. Learn to implement comprehensive tracing across distributed systems, understand the OTel architecture, and integrate with various backends. Develop expertise in advanced tracing patterns, performance optimization, and building vendor-neutral observability solutions that provide deep insights into complex microservices architectures.

Learning Resources

Course Title	Provider	Description	Level	Action
OpenTelemetry (OTel) Deep Dive	OpenTelemetry	Understanding the architecture and components of OpenTelemetry, the emerging industry standard for instrumentation	Advanced	Getting Started
OpenTelemetry Concepts Guide	Coralogix	Explains the core components (API, SDK, Collector), data signals (Traces, Metrics, Logs), and adoption guide	Advanced	Concepts Guide
Jaeger In-Depth Guide	Uptrace	Comprehensive guide explaining Jaeger's architecture, deployment, instrumentation, and UI for distributed tracing	Intermediate	In-Depth Guide
Jaeger Implementation Video	YouTube	Step-by-step video guide for deploying Jaeger with Docker and using the Jaeger UI to view and analyze traces	Intermediate	Implementation Video
Implement Alerting	Elastic	Configure rules to automatically detect critical conditions and trigger notifications with connectors in Elasticsearch	Intermediate	Elastic Alerting
GCP Log-Based Alerts	Google Cloud	Creating log-based and metric-based alerting policies in Google Cloud with notification channels	Intermediate	GCP Alerts

Hands-On Activities

OpenTelemetry Integration: Implement OTel instrumentation across your microservices architecture
Advanced Tracing Setup: Deploy Jaeger with OpenTelemetry collector for comprehensive trace analysis
Cross-Service Correlation: Implement distributed tracing that correlates requests across all services
Performance Analysis: Use tracing data to identify and optimize performance bottlenecks