Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decoupling the Monolith
• Avoiding premature decomposition
• Decomposition By Layer
• Leveraging Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting the appropriate base image
• Minimizing the number of layers
• Utilizing multi-stage builds
• Image optimization (e.g., sorting multi-line arguments)
• Maximizing build cache efficiency
• Pinning image versions
• Fine-tuning resource allocation
• Adhering to secure container practices
• Configuring runtime for optimal performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Creating and executing an Initial Deployment
• Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding Rolling Update mechanisms
• Creating and executing a Rolling Update
• Rolling Back Deployments
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 Jobs and CronJobs
Performing Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Automating Common Tasks in Kubernetes with Python
• Using Python for administrative operations in Kubernetes
• Defining Configuration objects with Python
• Creating Deployment objects with Python
• Watching Kubernetes Events using Python
• Scaling Deployments via Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Managing Configuration Integrity
Adopting the GitOps Approach for Deployment Automation
• GitOps Principles
• Introduction to Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring 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 for 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 of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation
• Log Visualization
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Utilizing OpenTelemetry
• Distributed Tracing Tools
• Instrumenting Applications
• Leveraging Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Observability Concepts
• Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Processing Logs
• Filtering and Enriching Logs
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding various failure types in cluster environments
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Network Issues
• DNS Failures and Application Timeout Handling
• Simulating API Server Outages
• Simulating High Traffic for System Stability
• Storage Failures
• Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• K8sGPT CLI Architecture
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster with K8sGPT
• Analyzing Real-Time Issues using K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Basic knowledge of Linux command line
- Experience with application development or system administration
- Familiarity with containers (Docker concepts)
- 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
