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
• Pitfalls of Premature Decomposition
• Decomposition By Layer
• Utilizing Decomposition Patterns (Strangler Fig, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the appropriate base image
• Minimizing the number of layers
• Implementing multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Leveraging the build cache effectively
• Pinning specific image versions
• Fine-tuning resource allocation
• Adhering to secure container practices
• Optimizing runtime configuration for performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Creating and executing an Initial Deployment
• Available Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding Rolling Updates
• Creating and executing a Rolling Update
• Initiating a Deployment 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 a Job and CronJob
Conducting Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques using kubectl
Module 4: Automation & Operational Efficiency
Automating Common Tasks in Kubernetes with Python
• Using Python for administrative operations in Kubernetes
• Defining Configuration objects via Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events with Python
• Scaling Deployments programmatically with Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration within Kubernetes
• Ensuring Configuration Integrity
Implementing GitOps for Automated Deployments
• Core GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing 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 Metrics for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Strategies
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Concepts of Distributed Tracing
• Utilizing OpenTelemetry
• Tools for Distributed Tracing
• Instrumenting an Application
• Using Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Overview of Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Log Processing Methods
• Filtering and Enriching Logs
• Event Sourcing Approaches
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various types of failures in a cluster environment
• Simulating Node Failures
• Scenarios for Pod Eviction & Resource Exhaustion
• 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
• Benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage Guidelines
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster using K8sGPT
• Diagnosing Real-Time Issues with K8sGPT
• Deploying 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
