Course Outline
Module 1: Microservices Design
• Defining appropriate Microservice Boundaries
• Implementing Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decoupling the Monolith
• Avoiding premature decomposition
• Decomposition By Layer
• Applying Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the optimal base image
• Reducing the number of layers
• Utilizing multi-stage builds
• Optimizing images (sorting multi-line arguments, etc.)
• Maximizing build cache usage
• Locking image versions
• Tuning resource allocation
• Adopting secure container practices
• Configuring runtime for enhanced performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Initializing and executing a Deployment
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding Rolling Update mechanics
• Implementing and executing a Rolling Update
• Rolling back a Deployment
Implementing Canary Deployments
• Grasping Canary Deployment concepts
• Setting up and executing a Canary Deployment
Implementing Blue-Green Deployments
• Understanding Blue-Green Deployment logic
• Setting up and executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Executing Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Leveraging Python to Automate Common Tasks in Kubernetes
• Performing administrative operations in Kubernetes using Python
• Defining Configuration objects via Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events with Python
• Scaling a Deployment using Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Maintaining the Integrity of Configuration
Adopting the GitOps Approach for Automating Deployments
• Core GitOps Principles
• Introduction to Flux
• Installing Flux to a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring the Source Repository
Handling Application Updates with Image Automation
• Updating an Application Deployment with Flux
• Scanning Container Image Repositories for Tags
• Defining Policy 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
• Pod and Container Logs
• Control Plane Logs
• Resource Usage of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation
• Log Visualization
Distributed Tracing in Kubernetes
• The concept of distributed tracing
• Implementing OpenTelemetry
• Distributed Tracing Tools
• Instrumenting an Application
• Utilizing Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Observability concepts
• Monitoring Tools
• Using Prometheus Instrumentation
Advanced Use Cases for Logging
• Processing Logs
• Filtering and Enriching Logs
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Recognizing various failure types in cluster environments
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Network Issues
• Handling application timeouts via DNS failures
• Simulating an API Server Outage
• Simulating high traffic for system stability testing
• Storage Failure scenarios
• Configuration Errors
• Understanding Incident reporting procedures
Module 7: AI To support Troubleshooting
• Advantages of Generative AI for Kubernetes
• K8sGPT CLI architecture
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster using K8sGPT
• Analyzing Real-Time Issues using K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Fundamental proficiency with the Linux command line
- Practical experience in application development or system administration
- Working knowledge of container technologies (Docker concepts)
- Basic grasp of Kubernetes fundamentals (pods, deployments, services)
- General understanding of software architecture (e.g., APIs, services)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers specializing in microservices
- Cloud Engineers and Platform Engineers
-
System Administrators moving into Kubernetes-based roles
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
