Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decoupling the Monolith
• Risks of Premature Decomposition
• Decomposition by Layer
• Utilizing Decomposition Patterns (Strangler Fig, Parallel Run, Feature Toggle)
• Considerations for Data Decomposition (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting appropriate base images
• Minimizing the number of layers
• Implementing multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Maximizing the build cache efficiency
• Pinning image versions for stability
• Fine-tuning resource allocation
• Secure container practices
• Configuring the 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 the Rolling Update mechanism
• 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
Automating Common Tasks in Kubernetes Using Python
• Performing administrative operations in Kubernetes with Python
• Defining Configuration objects using Python
• Creating Deployment objects with Python
• Monitoring Kubernetes Events via Python
• Scaling Deployments using Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Maintaining Configuration Integrity
Adopting GitOps for Automated Deployments
• Core GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Setting up Notifications
• Structuring the Source Repository
Managing Application Updates with Image Automation
• Updating an Application Deployment via Flux
• Scanning Container Image Repositories for new 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
• Resource Usage Analysis for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation
• Log Visualization
Distributed Tracing in Kubernetes
• Concept of Distributed Tracing
• Utilizing OpenTelemetry
• Tools for Distributed Tracing
• Instrumenting an Application
• Identifying Performance Issues via Tracing
Monitoring with Prometheus and Grafana
• Observability Concepts
• Monitoring Tooling
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Log Processing
• Filtering and Enriching Logs
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various types of failures in cluster environments
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Network Issues
• DNS Failures and Application Timeout Handling
• Simulating API Server Outages
• Simulating High Traffic for System Stability Testing
• Storage Failures
• Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI to Support Troubleshooting
• Advantages of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage Guide
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster with K8sGPT
• Addressing Real-Time Issues Using K8sGPT
• The In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Experience in application development or system administration
- Familiarity with container concepts (Docker)
- Basic understanding of Kubernetes concepts (pods, deployments, services)
- General comprehension 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
