Course Outline
Module 1: Microservices Design
• Defining Effective Microservice Boundaries
• Leveraging Domain Driven Design (DDD)
• Alternative Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• Recognising Premature Decomposition
• Decomposition by Layer
• Employing 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
• Utilising Multi-Stage Builds
• Image Optimization Techniques (e.g., sorting multi-line arguments)
• Maximising Build Cache Efficiency
• Pinning Specific Image Versions
• Fine-Tuning Resource Allocation
• Implementing Secure Container Practices
• Configuring 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 Executing a Rolling Update
• Performing Deployment Rollbacks
Executing Canary Deployments
• Understanding Canary Deployment Concepts
• Creating and Executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding Blue-Green Deployment Concepts
• Creating and Executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating Jobs and CronJobs
Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques using 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 using Python
• Monitoring Kubernetes Events with Python
• Scaling Deployments Programmatically with Python
Addressing Automation Challenges
• Declarative Configuration in Kubernetes
• Maintaining Configuration Integrity
Adopting the GitOps Approach for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux within 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 Tags
• Defining Policies for Latest Image Selection
• Configuring Flux for Automated 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
• Monitoring Resource Usage for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Techniques
• Log Visualization Methods
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Implementing OpenTelemetry
• Utilizing Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Leveraging Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Essential Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Logging Use Cases
• Log Processing Strategies
• Filtering and Enriching Logs
• Event Sourcing Techniques
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding Various Failure Types in Cluster Environments
• Simulating Node Failures
• Managing Pod Eviction and Resource Exhaustion Scenarios
• Addressing Network Issues
• Handling DNS Failures and Application Timeouts
• Simulating API Server Outages
• Testing System Stability Under High Traffic Loads
• Dealing with Storage Failures
• Resolving Configuration Errors
• Adhering to Incident Reporting Procedures
Module 7: AI To support Troubleshooting
• Advantages of Generative AI for Kubernetes Operations
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage Guidelines
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Conducting Cluster Analysis with K8sGPT
• Investigating Real-Time Issues using 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 components (pods, deployments, services)
- General comprehension of software architecture (e.g., APIs, services)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend or Software Developers working with microservices
- Cloud 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
