Course Outline
Module 1: Microservices Design
• Establishing effective microservice boundaries
• Applying Domain-Driven Design (DDD) principles
• Exploring alternatives to business domain boundaries (Volatility, Data, Technology, Organizational)
• Strategies for splitting monoliths
• Avoiding premature decomposition
• Layer-based decomposition techniques
• Utilizing decomposition patterns (Strangler Fig, Parallel Run, Feature Toggles)
• Addressing data decomposition concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting appropriate base images
• Reducing the number of image layers
• Implementing multi-stage builds
• Image optimization techniques (ordering multi-line arguments, etc.)
• Maximizing build cache efficiency
• Pinning specific image versions
• Fine-tuning resource allocation settings
• Adhering to 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
Executing Rolling Update Deployments
• Understanding the mechanics of rolling updates
• Creating and applying a rolling update strategy
• Reverting deployments when necessary
Executing Canary Deployments
• Understanding canary release principles
• Creating and deploying a canary release
Executing Blue-Green Deployments
• Understanding blue-green deployment strategies
• Creating and implementing a blue-green deployment
Running Jobs and CronJobs
• Setting up Jobs and CronJobs
Monitoring and Troubleshooting Tasks
• Employing kubectl for troubleshooting techniques
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks with Python
• Using Python for administrative operations in Kubernetes
• Defining Configuration objects via Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events with Python scripts
• Scaling Deployments programmatically
Navigating Challenges in Automation
• Embracing declarative configuration with Kubernetes
• Maintaining configuration integrity
Implementing GitOps for Automated Deployments
• Core GitOps principles
• Introducing the Flux tool
• Installing Flux into a Kubernetes cluster
Configuring Flux for Automated Workflows
• Setting up 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 selecting latest images
• 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
• Reviewing Pod and Container logs
• Analyzing Control Plane logs
• Monitoring resource usage on Nodes and Pods
Collecting and Analyzing Logs
• Log aggregation techniques
• Visualizing log data
Distributed Tracing in Kubernetes
• Defining distributed tracing
• Implementing OpenTelemetry
• Utilizing distributed tracing tools
• Instrumenting applications for tracing
• Using traces to identify performance issues
Monitoring with Prometheus and Grafana
• Core observability concepts
• Overview of monitoring tools
• Implementing Prometheus instrumentation
Advanced Logging Use Cases
• Processing log data
• Filtering and enriching logs
• Applying Event Sourcing patterns
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various failure types in cluster environments
• Simulating node failures
• Simulating pod eviction and resource exhaustion scenarios
• Addressing network issues
• Handling DNS failures to manage application timeouts
• Simulating API server outages
• Testing system stability under high traffic loads
• Simulating storage failures
• Identifying configuration errors
• Understanding incident reporting procedures
Module 7: AI To Support Troubleshooting
• Benefits of Generative AI for Kubernetes operations
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• Overview of K8sGPT commands and usage
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing cluster health with K8sGPT
• Investigating real-time issues using K8sGPT
• Deploying the in-cluster operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line interface
- Practical experience in application development or system administration
- Familiarity with containerization concepts, particularly Docker
- Basic understanding of Kubernetes fundamentals (pods, deployments, services)
- General comprehension of software architecture principles (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
