Course Outline
Module 1: Microservices Design
• Establishing Effective Microservice Boundaries
• Utilizing Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decomposing the Monolith
• Avoiding Premature Decomposition
• Decomposition By Layer
• Applying Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the appropriate base image
• Reducing the number of layers
• Employing multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Maximizing the build cache
• Pinning image versions for stability
• Fine-tuning resource allocation
• Adhering to secure container practices
• Configuring runtime settings for optimal 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 the Rolling Update mechanism
• Creating and executing a Rolling Update
• Performing 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
Managing Jobs and CronJobs
• Creating a Job and CronJob
Performing Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques using kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks with Python
• Using Python for administrative operations in Kubernetes
• Defining Configuration objects with Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events via Python
• Scaling Deployments programmatically
Understanding the Challenges of Automating Deployments
• Declarative Configuration in Kubernetes
• Maintaining Configuration Integrity
Adopting the GitOps Approach for Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux on a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring the Source Repository
Managing Application Updates with Image Automation
• Updating Application Deployments via Flux
• Scanning Container Image Repositories for new 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
• Pod and Container Logs
• Control Plane Logs
• Resource Usage for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Strategies
• Log Visualization Tools
Distributed Tracing in Kubernetes
• Defining Distributed Tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting Applications
• Identifying Performance Issues via Tracing
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Selection of Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Log Processing Techniques
• Filtering and Enriching Logs
• Event Sourcing Patterns
Module 6: Cluster Crisis Simulation & Incident Response
• Recognizing various types of cluster environment failures
• Simulating Node Failures
• Scenarios involving Pod Eviction & Resource Exhaustion
• Addressing Network Issues
• Managing DNS failures and application timeouts
• Simulating API Server Outages
• Stress-testing system stability with high traffic
• Handling Storage Failures
• Resolving Configuration Errors
• Comprehending 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 Guidelines
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Conducting Cluster Analysis with K8sGPT
• Addressing Real-Time Issues via K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Practical experience in application development or system administration
- Knowledge of container concepts (Docker)
- Basic understanding of Kubernetes principles (pods, deployments, services)
- General grasp 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 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
