Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Applying Domain-Driven Design (DDD)
• Alternative Boundary Strategies (Volatility, Data, Technology, Organizational)
• Decoupling the Monolith
• Avoiding Premature Decomposition
• Layer-Based Decomposition
• Employing Decomposition Patterns (Strangler Fig, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Issues (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting appropriate base images
• Reducing layer counts
• Utilizing multi-stage builds
• Image Optimization Techniques (e.g., sorting multi-line arguments)
• Maximizing Build Cache Efficiency
• Locking Image Versions
• Fine-Tuning Resource Allocation
• Implementing Secure Container Practices
• Optimizing Runtime Configuration for Performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Executing Initial Deployments
• Key Deployment Options in Kubernetes
Executing Rolling Update Deployments
• Understanding the Rolling Update Mechanism
• Creating and Executing a Rolling Update
• Performing Deployment Rollbacks
Executing Canary Deployments
• Understanding Canary Releases
• Creating and Executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding Blue-Green Architecture
• Creating and Executing a Blue-Green Deployment
Managing Jobs and CronJobs
• Setting up Jobs and CronJobs
Conducting Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques Using kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks with Python
• Performing Administrative Operations via Python
• Defining Configuration Objects with Python
• Creating Deployment Objects Using Python
• Monitoring Kubernetes Events in Python
• Scaling Deployments Programmatically
Addressing Challenges in Automating Deployments
• Declarative Configuration in Kubernetes
• Ensuring Configuration Integrity
Implementing GitOps for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux on a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring the Source Repository
Managing Application Updates with Image Automation
• Updating Application Deployments via Flux
• Scanning Container Registries 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
• Monitoring Resource Usage on Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Strategies
• Log Visualization Techniques
Distributed Tracing in Kubernetes
• Introduction to Distributed Tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Application Instrumentation
• Identifying Performance Issues via Tracing
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Key Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Logging Use Cases
• Log Processing Methods
• Filtering and Enriching Logs
• Event Sourcing Techniques
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding Various Failure Types in Cluster Environments
• Simulating Node Failures
• Scenarios: Pod Eviction and Resource Exhaustion
• Network Disruptions
• DNS Failures and Application Timeout Management
• Simulating API Server Outages
• Testing System Stability Under High Traffic
• Storage Failures
• Configuration Errors
• Incident Reporting Protocols
Module 7: AI Support for Troubleshooting
• Advantages of Generative AI in Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• Commands and Usage of K8sGPT
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Cluster Analysis Using K8sGPT
• Real-Time Issue Analysis with K8sGPT
• 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 grasp of software architecture principles (e.g., APIs, services)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers specializing in 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
