Course Outline
Module 1: Microservices Design
• Defining Effective Microservice Boundaries
• Applying Domain-Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Splitting the Monolith
• Risks of Premature Decomposition
• Decomposition by Layer
• Using Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimising Docker and the Runtime
• Choosing the Right Base Image
• Minimising the Number of Layers
• Using Multi-Stage Builds
• Image Optimisation (Sorting Multi-line Arguments, etc.)
• Leveraging the Build Cache
• Pinning Image Versions
• Fine-Tuning Resource Allocation
• Secure Container Practices
• Runtime Configuration for Performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Creating and Executing an Initial Deployment
• Kubernetes Deployment Options
Performing Rolling Update Deployments
• Understanding Rolling Updates
• Creating and Executing a Rolling Update
• Rolling Back a Deployment
Performing Canary Deployments
• Understanding Canary Deployments
• Creating and Executing a Canary Deployment
Performing Blue-Green Deployments
• Understanding Blue-Green Deployments
• 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 to Perform Administrative Operations in Kubernetes
• Using Python to Define Configuration Objects
• Using Python to Create Deployment Objects
• Watching Kubernetes Events Using Python
• Scaling a Deployment Using Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Managing the Integrity of Configuration
Using the GitOps Approach for Automating Deployments
• GitOps Principles
• Introducing Flux
• Installing Flux in a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Using Notifications
• The Source Repository Structure
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
• Why Logging and Tracing Are Important
• Accessing Kubernetes Logs
• Pod and Container Logs
• Control Plane Logs
• Resource Usage of Nodes and Pods
Collecting and Analysing the Logs
• Log Aggregation
• Log Visualisation
Distributed Tracing in Kubernetes
• What Is Distributed Tracing
• Using OpenTelemetry
• Distributed Tracing Tools
• Instrumenting an Application
• Using 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
• Understanding Different Types of Failures in a Cluster Environment
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Network Issues
• DNS Failures for Application Timeout Handling
• Simulating an API Server Outage
• Simulating High Traffic for System Stability
• Storage Failures
• Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI to Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• K8sGPT CLI Architecture
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage
• Using K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analysing the Cluster Using K8sGPT
• Analysing Real-Time Issues Using K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Basic knowledge of the Linux command line
- Experience with application development or system administration
- Familiarity with containers (Docker concepts)
- Basic understanding of Kubernetes concepts (pods, deployments, services)
- General understanding of software architecture (e.g. APIs, services)
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
