Course Outline
Module 1: Microservices Design
• Defining Effective Microservice Boundaries
• Applying Domain-Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Decomposing the Monolith
• Avoiding Premature Decomposition
• Decomposition By Layer
• Utilising 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
• Minimising Image Layers
• Implementing Multi-Stage Builds
• Optimising Images (Sorting multi-line arguments, etc.)
• Leveraging Build Caches
• Pinning Image Versions
• Fine-Tuning Resource Allocation
• Secure Container Practices
• Runtime Configuration for Performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Creating and Executing an Initial Deployment
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding Rolling Updates
• Creating and Executing a Rolling Update
• Rolling Back a Deployment
Performing Canary Deployments
• Understanding Canary Releases
• Creating and Executing a Canary Deployment
Performing Blue-Green Deployments
• Understanding Blue-Green Releases
• Creating and Executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Conducting Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks with Python
• Performing Administrative Operations in Kubernetes using Python
• Defining Configuration Objects with Python
• Creating Deployment Objects with Python
• Watching Kubernetes Events using Python
• Scaling a Deployment using Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration in Kubernetes
• Maintaining Configuration Integrity
Employing the GitOps Approach for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux on a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilising Notifications
• Structuring the Source Repository
Managing Application Updates via 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 Analysing Logs
• Log Aggregation
• Log Visualisation
Distributed Tracing in Kubernetes
• What is Distributed Tracing
• Using OpenTelemetry
• Distributed Tracing Tools
• Instrumenting an Application
• Identifying Performance Issues via Tracing
Monitoring with Prometheus and Grafana
• Observability Concepts
• Monitoring Tools
• Using 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
• Handling Application Timeouts via DNS Failures
• Simulating an API Server Outage
• 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
• Using K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster using K8sGPT
• Analyzing Real-Time Issues using K8sGPT
• In-Cluster Operator for K8sGPT
Requirements
- Fundamental proficiency with the Linux command line
- Practical experience in application development or system administration
- A working knowledge of container concepts (such as Docker)
- A foundational grasp of Kubernetes components (pods, deployments, services)
- A general understanding of software architecture (e.g., APIs, services)
Target audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend and Software Developers engaged with microservices
- Cloud Engineers and Platform Engineers
-
System Administrators moving towards 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
