Course Outline
Module 1: Microservices Design
• Defining effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• The Risks of Premature Decomposition
• Decomposition By Layer
• Utilizing Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the appropriate base image
• Minimizing the number of layers
• Leveraging multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Maximizing the efficiency of the build cache
• Pinning specific image versions
• Fine-tuning resource allocation
• Implementing secure container practices
• Optimizing runtime configuration for performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Creating and executing an Initial Deployment
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding the mechanics of Rolling Updates
• Creating and executing a Rolling Update
• Performing Deployment Rollbacks
Executing Canary Deployments
• Understanding the Canary Deployment model
• Creating and executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding the Blue-Green Deployment model
• Creating and executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Executing Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques using kubectl
Module 4: Automation & Operational Efficiency
Automating Common Tasks in Kubernetes with Python
• Using Python for administrative operations in Kubernetes
• Using Python to define Configuration objects
• Using Python to create Deployment objects
• Monitoring Kubernetes Events with Python
• Scaling Deployments programmatically with Python
Addressing the Challenges of Automating Deployments
• Declarative Configuration in Kubernetes
• Ensuring Configuration Integrity
Adopting the GitOps Approach for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring the Source Repository
Managing Application Updates with Image Automation
• Updating Application Deployments via Flux
• Scanning Container Image Repositories for tags
• Defining policies for Latest Image selection
• Configuring Flux to perform automatic image updates
Module 5: Observability & Root Cause Clarity
Kubernetes Logging and Tracing Capabilities
• The Importance of Logging and Tracing
• Accessing Kubernetes Logs
• Examining Pod and Container Logs
• Reviewing Control Plane Logs
• Monitoring Resource Usage of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation strategies
• Log Visualization techniques
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Implementing OpenTelemetry
• Utilizing Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Leveraging Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Overview of Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Logging Use Cases
• Processing Logs
• Filtering and Enriching Logs
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various failure types in cluster environments
• Simulating Node Failures
• Simulating Pod Eviction & Resource Exhaustion Scenarios
• Investigating Network Issues
† Handling DNS failures to manage application timeouts
• Simulating an API Server Outage
• Simulating High Traffic to test System Stability
• Simulating Storage Failures
• Simulating Configuration Errors
• Mastering Incident Reporting Procedures
Module 7: AI to Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster with K8sGPT
• Diagnosing Real-Time Issues using K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line interface
- Practical experience in application development or system administration
- Familiarity with containerization concepts, specifically Docker
- Basic grasp of Kubernetes core concepts (pods, deployments, services)
- General understanding of software architecture patterns (e.g., APIs, microservices)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers working with microservices
- Cloud Engineers and Platform Engineers
-
System Administrators transitioning to Kubernetes environments
Testimonials (2)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
