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
• Avoiding Premature Decomposition
• Decomposition by Layer
• Utilizing Decomposition Patterns (Strangler Fig, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the Appropriate Base Image
• Reducing Layer Count
• Implementing 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
• Adhering to Secure Container Practices
• Configuring Runtime for Peak Performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Establishing and Executing an Initial Deployment
• Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding the Mechanics 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
• Troubleshooting Techniques with kubectl
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 via Python
• Monitoring Kubernetes Events using Python
• Scaling Deployments via Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Maintaining Configuration Integrity
Applying the GitOps Approach for Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Leveraging Notifications
• Structure of the Source Repository
Managing Application Updates with Image Automation
• Updating Application Deployments 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
• Viewing Pod and Container Logs
• Reviewing Control Plane Logs
• Analyzing Resource Usage for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Strategies
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Defining 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
• Log Processing Methods
• Filtering and Enriching Logs
• Event Sourcing Patterns
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding Various Failure Types in Cluster Environments
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Addressing Network Issues
• Handling DNS Failures and Application Timeouts
• Simulating API Server Outages
• Simulating High Traffic for System Stability Testing
• Managing Storage Failures
• Resolving Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI to Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• Architecture of K8sGPT CLI
• Installation of the K8sGPT CLI
• Usage and Commands of K8sGPT
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing Clusters with K8sGPT
• Diagnosing Real-Time Issues using K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Experience in application development or system administration
- Familiarity with container concepts (e.g., Docker)
- Basic understanding of Kubernetes components (pods, deployments, services)
- General grasp 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
