Course Outline
Module 1: Microservices Design
• Establishing Effective Microservice Boundaries
• Leveraging Domain-Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decomposing the Monolith
• Avoiding Premature Decomposition
• Decomposition by Layer
• Employing Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting appropriate base images
• Reducing layer count
• Implementing 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
• Adhering to secure container practices
• Optimizing runtime configuration for performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Executing Initial Deployments
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding Rolling Updates
• Creating and executing a Rolling Update
• Reverting Deployments via Rollback
Executing Canary Deployments
• Understanding Canary Deployments
• Creating and executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding Blue-Green Deployments
• Creating and executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating Job and CronJob resources
Conducting Monitoring and Troubleshooting Tasks
• Applying troubleshooting techniques with kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks with Python
• Performing administrative operations via Python
• Defining Configuration objects using Python
• Creating Deployment objects with Python
• Monitoring Kubernetes Events through Python
• Scaling Deployments programmatically
Understanding the Challenges of Automating Deployments
• Managing Declarative Configuration in Kubernetes
• Ensuring Configuration Integrity
Implementing GitOps for Automated Deployments
• Core GitOps Principles
• Introduction to Flux
• Installing Flux onto a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notification Systems
• Structuring the Source Repository
Managing Application Updates via Image Automation
• Updating Deployments with Flux
• Scanning Container Registries for Tags
• Defining Policies for Latest Image Selection
• Configuring Flux for 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 for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Methods
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Introduction to Distributed Tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Using Traces to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Selection of Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Log Processing Strategies
• Filtering and Enriching Logs
• Implementing Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Recognizing various failure types in cluster environments
• Simulating Node Failures
• Scenarios involving Pod Eviction and Resource Exhaustion
• Addressing Network Issues
• Handling DNS Failures and Application Timeouts
• Simulating API Server Outages
• Stress Testing with High Traffic for Stability
• Dealing with Storage Failures
• Diagnosing Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI to Support Troubleshooting
• Advantages of Generative AI in Kubernetes Management
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage Guide
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Cluster Analysis via K8sGPT
• Investigating Real-Time Issues with K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Hands-on experience in application development or system administration
- Understanding of container concepts (specifically Docker)
- Basic familiarity with Kubernetes fundamentals (pods, deployments, services)
- General grasp of software architecture principles (e.g., APIs, services)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers engaged with microservices
- Cloud Engineers and Platform Engineers
-
System Administrators moving into 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
