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Kursplan
Module 1: Microservices Design
• A good Microservice Boundary
• Using Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Splitting the Monolith
• Premature decomposition
• Decomposition By Layer
• Using Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Choosing the right base image
• Minimizing the number of layers
• Using multi-stage builds
• Image optimization (sort 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 Update
• Creating and executing a Rolling Update
• Rolling Back 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 Task 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 to 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 Are Logging and Tracing Important
• Accessing the Kubernetes Logs
• Pod and Container Logs
• Control Plane Logs
• Resource Usage of Nodes and Pods
Collecting and Analyzing the Logs
• Log Aggregation
• Log Visualization
Distributed Tracing in Kubernetes
• What is distributed tracing
• Using OpenTelemetry
• Distributed Tracing Tools
• Instrumenting an Application
• Using Tracing to Find Performance Issues
Monitoring with Prometheus and Grafana
• Observability concepts
• Monitoring Tools
• Using Prometheus Instrumentation
Advanced Uses Cases for Logging
• Processing Logs
• Filtering and Enriching the Logs
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding the different types of failures in a cluster environment
• Simulating a Node Failures
• Pod Eviction & Resource Exhaustion Scenario
• Network Issues
• DNS failures to for application timeout handling
• Simulating an API Server Outage
• Simulating high traffic for system stability
• Storage Failure
• Configuration Errors
• Understanding Incident reporting procedures
Module 7: AI To support Troubleshooting
• Benefits of Generative AI for Kubernetes
• K8sGPT CLI architecture
• Install the K8sGPT CLI
• K8sGPT Commands and Usage
• Using K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster using K8sGPT
• Analyzing Real-Time Issues using K8sGPT
• In-Cluster Operator for K8sGPT
Krav
- Basic knowledge of 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
49 Timmar
Vittnesmål (1)
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