Course Outline
Module 1: Microservices Design
• Defining effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Exploring 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
• Minimizing layer count
• Implementing multi-stage builds
• Image optimization techniques (e.g., sorting multi-line arguments)
• Maximizing build cache efficiency
• Pinning specific image versions
• Fine-tuning resource allocation
• 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
• Key Kubernetes Deployment Options
Performing Rolling Update Deployments
• Understanding the Mechanics of Rolling Updates
• Creating and executing a Rolling Update
• Initiating Deployment Rollbacks
Performing Canary Deployments
• Understanding Canary Release Mechanisms
• Creating and executing a Canary Deployment
Performing Blue-Green Deployments
• Understanding Blue-Green Architecture
• Creating and executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Performing Monitoring and Troubleshooting Tasks
• Employing Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Using Python to Automate Common Task in Kubernetes
• Leveraging Python for administrative operations in Kubernetes
• Utilizing Python to define Configuration objects
• Using Python to create Deployment objects
• Watching Kubernetes Events via Python
• Scaling Deployments using Python scripts
Understanding the Challenges of Automating Deployments
• Declarative Configuration within Kubernetes
• Maintaining Configuration Integrity
Using the GitOps Approach for Automating Deployments
• Core GitOps Principles
• Introducing Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notification mechanisms
• Structuring the Source Repository
Handling Application Updates with Image Automation
• Updating an Application Deployment via 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 Metrics for Nodes and Pods
Collecting and Analyzing the Logs
• Log Aggregation Strategies
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Defining Distributed Tracing
• Implementing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Utilizing Tracing Data to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Overview of Monitoring Tools
• Applying Prometheus Instrumentation
Advanced Uses 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
• Scenario: Pod Eviction & Resource Exhaustion
• Diagnosing Network Issues
• Handling DNS failures and application timeouts
• Simulating an API Server Outage
• Stress Testing with High Traffic for System Stability
• Storage Failure Scenarios
• Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To support Troubleshooting
• Benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installation Guide for the K8sGPT CLI
• K8sGPT Commands and Usage Scenarios
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Comprehensive Cluster Analysis using K8sGPT
• Real-Time Issue Analysis with K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Practical experience in application development or system administration
- Familiarity with containerization concepts (specifically Docker)
- Basic understanding of Kubernetes primitives (pods, deployments, services)
- General grasp of software architecture principles (e.g., APIs, microservices)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers focused on microservices
- Cloud and Platform Engineers
-
System Administrators transitioning to Kubernetes-based 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
