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
• 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
• Reducing the Number of Layers
• Leveraging Multi-Stage Builds
• Image Optimization Techniques (e.g., sorting multi-line arguments)
• Maximizing Build Cache Utilization
• Pinning Image Versions for Stability
• Fine-Tuning Resource Allocation
• Implementing Secure Container Practices
• Configuring Runtime for Optimal Performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Creating and Executing an Initial Deployment
• Exploring Kubernetes Deployment Options
Performing Rolling Update Deployments
• Understanding Rolling Update Mechanics
• Creating and Executing a Rolling Update
• Executing Rolling Back Deployment
Performing Canary Deployments
• Understanding Canary Deployment Concepts
• Creating and Executing a Canary Deployment
Performing Blue-Green Deployments
• Understanding Blue-Green Deployment Concepts
• Creating and Executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Performing Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Using Python to Automate Common Tasks in Kubernetes
• Using Python for Administrative Operations in Kubernetes
• Using Python to Define Configuration Objects
• Using Python to Create Deployment Objects
• Monitoring Kubernetes Events via Python
• Scaling Deployments Programmatically Using Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Ensuring Configuration Integrity
Using 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 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
• Pod and Container Logs
• Control Plane Logs
• Monitoring Resource Usage of Nodes and Pods
Collecting and Analyzing the Logs
• Log Aggregation Techniques
• Log Visualization Methods
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Implementing OpenTelemetry
• Popular Distributed Tracing Tools
• Instrumenting an Application for Tracing
• Using Tracing Data to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Essential Monitoring Tools
• Utilizing Prometheus Instrumentation
Advanced Use Cases for Logging
• Processing Logs Efficiently
• Filtering and Enriching Logs
• Implementing Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding Various Failure Types in Cluster Environments
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Addressing Network Issues
• Handling DNS Failures and Application Timeouts
• Simulating an API Server Outage
• Simulating High Traffic Loads for System Stability Testing
• Managing Storage Failures
† Resolving Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installation Guide for K8sGPT CLI
• K8sGPT Commands and Usage Instructions
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing Cluster Health Using K8sGPT
• Diagnosing Real-Time Issues with K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line interface
- Prior experience in application development or system administration
- Familiarity with containerization concepts (Docker)
- Basic understanding of Kubernetes components (pods, deployments, services)
- General grasp of software architecture principles (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
