Course Outline
Module 1: Microservices Design
• Defining an Effective Microservice Boundary
• Applying Domain Driven Design (DDD)
• Alternative Approaches to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• Risks of Premature Decomposition
• Decomposition By Layer
• Utilising Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the Appropriate Base Image
• Minimising Layer Count
• Implementing Multi-Stage Builds
• Image Optimisation Techniques (e.g., Consolidating Multi-line Arguments)
• Maximising Build Cache Utilisation
• Pinning Image Versions for Stability
• Fine-Tuning Resource Allocation
• Adhering to Secure Container Practices
• Optimising Runtime Configuration for Performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Executing Initial Deployments
• Exploring Kubernetes Deployment Options
Performing Rolling Update Deployments
• Comprehending Rolling Updates
• Executing a Rolling Update
• Managing Deployment Rollbacks
Executing Canary Deployments
• Understanding Canary Deployment Mechanics
• Planning and Executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding Blue-Green Deployment Strategies
• Planning and Executing a Blue-Green Deployment
Managing Jobs and CronJobs
• Creating Jobs and CronJobs
Conducting Monitoring and Troubleshooting Tasks
• Employing kubectl for Diagnostic Techniques
Module 4: Automation & Operational Efficiency
Leveraging Python to Automate Common Kubernetes Tasks
• Automating Administrative Operations in Kubernetes with Python
• Defining Configuration Objects via Python
• Generating Deployment Objects using Python
• Monitoring Kubernetes Events through Python Scripts
• Scaling Deployments Programmatically with Python
Addressing the Challenges of Automated Deployments
• Implementing Declarative Configuration in Kubernetes
• Ensuring Configuration Integrity
Adopting the GitOps Approach for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilising Notification Mechanisms
• Structuring the Source Repository
Managing Application Updates via Image Automation
• Updating Application Deployments using Flux
• Scanning Container Image Repositories for Tags
• Establishing Policies for Latest Image Selection
• Configuring Flux to Execute Automatic Image Updates
Module 5: Observability & Root Cause Clarity
Kubernetes Logging and Tracing Capabilities
• The Importance of Logging and Tracing
• Accessing Kubernetes Logs
• Retrieving Pod and Container Logs
• Accessing Control Plane Logs
• Monitoring Resource Usage for Nodes and Pods
Collecting and Analysing Logs
• Log Aggregation Strategies
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Fundamentals of Distributed Tracing
• Implementing OpenTelemetry
• Available Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Utilising Tracing Data to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Overview of Monitoring Tools
• Applying Prometheus Instrumentation
Advanced Use Cases for Logging
• Log Processing Techniques
• Filtering and Enriching Logs
• Event Sourcing Patterns
Module 6: Cluster Crisis Simulation & Incident Response
• Identifying Various Failure Types in Cluster Environments
• Simulating Node Failures
• Scenarios Involving Pod Eviction & Resource Exhaustion
• Addressing Network Issues
• Managing DNS Failures and Application Timeout Handling
• Simulating API Server Outages
• Stress-Testing System Stability with High Traffic
• Responding to Storage Failures
• Rectifying Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To Support Troubleshooting
• Advantages of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage Guidelines
• Utilising K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Conducting Cluster Analysis via K8sGPT
• Investigating Real-Time Issues using K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of the Linux command line
- Practical experience in application development or system administration
- Proficiency with container concepts (e.g., Docker)
- Bearable grasp of Kubernetes fundamentals (pods, deployments, services)
- General understanding of software architecture (e.g., APIs, microservices)
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
