Course Outline
Module 1: Microservices Design
• Establishing effective microservice boundaries
• Applying Domain Driven Design (DDD)
• Alternative approaches to business domain boundaries (considering volatility, data, technology, and organizational factors)
• Strategies for splitting the monolith
• Avoiding premature decomposition
• Decomposition by layer
• Utilizing 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
• Minimizing the number of layers
• Implementing multi-stage builds
• Optimizing images (e.g., sorting multi-line arguments)
• Leveraging the build cache
• Pinning image versions
• Fine-tuning resource allocation
• Adhering to secure container practices
• Configuring the runtime for optimal performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Creating and executing an initial deployment
• Exploring Kubernetes deployment options
Executing Rolling Update Deployments
• Understanding rolling updates
• Creating and executing a rolling update
• Rolling back a deployment
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
Performing Monitoring and Troubleshooting Tasks
• Troubleshooting techniques using kubectl
Module 4: Automation & Operational Efficiency
Automating Common Tasks in Kubernetes Using Python
• Using Python for administrative operations in Kubernetes
• Defining configuration objects with Python
• Creating deployment objects using Python
• Watching Kubernetes events with Python
• Scaling deployments using Python
Understanding the Challenges of Automating Deployments
• Declarative configuration with Kubernetes
• Managing configuration integrity
Applying the GitOps Approach for Automating Deployments
• GitOps principles
• Introduction to Flux
• Installing Flux to a Kubernetes cluster
Configuring Flux for Automated Deployments
• Utilizing notifications
• Structuring the source repository
Handling Application Updates with Image Automation
• Updating an application deployment with Flux
• Scanning container image repositories for 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 of nodes and pods
Collecting and Analyzing Logs
• Log aggregation
• Log visualization
Distributed Tracing in Kubernetes
• Understanding distributed tracing
• Utilizing OpenTelemetry
• Distributed tracing tools
• Instrumenting an application
• Using tracing to identify performance issues
Monitoring with Prometheus and Grafana
• Observability concepts
• Monitoring tools
• Implementing Prometheus instrumentation
Advanced Use Cases for Logging
• Processing logs
• Filtering and enriching logs
• Event sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding different types of failures in a cluster environment
• Simulating node failures
• Scenarios involving pod eviction and resource exhaustion
• Addressing network issues
• Handling DNS failures and application timeouts
• Simulating an API server outage
• Simulating high traffic for system stability
• Managing storage failures
• Resolving configuration errors
• Understanding incident reporting procedures
Module 7: AI To Support Troubleshooting
• Benefits of generative AI for Kubernetes
• K8sGPT CLI architecture
• Installing the K8sGPT CLI
• K8sGPT commands and usage
• Utilizing K8sGPT analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the cluster using K8sGPT
• Analyzing real-time issues using K8sGPT
• In-cluster operator for K8sGPT
Requirements
- Basic knowledge of Linux command line
- Experience with application development or system administration
- Familiarity with containers (Docker concepts)
- Basic understanding of Kubernetes concepts (pods, deployments, services)
- General understanding 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
