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
• The risks of 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 appropriate base images
• Minimizing layer count
• Leveraging multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Maximizing build cache efficiency
• Pinning image versions for stability
• Fine-tuning resource allocation
• Implementing secure container practices
• Configuring runtime settings for 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 the concept of Rolling Updates
• Creating and executing a Rolling Update
• Performing Deployment Rollbacks
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
• Employing troubleshooting techniques with kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks Using Python
• Performing administrative operations in Kubernetes via Python
• Defining Configuration objects with Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events through Python
• Scaling Deployments programmatically
Understanding the Challenges of Automating Deployments
• Declarative Configuration within Kubernetes
• Maintaining Configuration Integrity
Implementing 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 Application Deployments 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
• Viewing Pod and Container logs
• Accessing Control Plane logs
• Monitoring Resource Usage of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation strategies
• Log Visualization techniques
Distributed Tracing in Kubernetes
• Understanding distributed tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting Applications for Tracing
• 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
• Processing logs
• Filtering and Enriching Logs
• Applying Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various failure types in cluster environments
• Simulating Node Failures
• Scenarios involving Pod Eviction and Resource Exhaustion
• Addressing Network Issues
• Handling DNS failures affecting application timeouts
• Simulating API Server Outages
• Stress-testing system stability with high traffic
• Managing Storage Failures
• Correcting Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To support Troubleshooting
• The benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• Commands and usage of K8sGPT
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing Cluster health using K8sGPT
• Investigating Real-Time Issues via 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 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
