Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• Avoiding Premature decomposition
• Decomposition By Layer
• Employing 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
• Reducing the number of layers
• Implementing multi-stage builds
• Image optimization (e.g., sorting multi-line arguments)
• Maximizing the build cache
• 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
• Executing an Initial Deployment
• Configuring Kubernetes Deployment Options
Performing Rolling Update Deployments
• Understanding the Rolling Update mechanism
• Executing a Rolling Update
• Initiating a Deployment Rollback
Performing Canary Deployments
• Understanding Canary Deployments
• Executing a Canary Deployment
Performing Blue-Green Deployments
• Understanding Blue-Green Deployments
• Executing a Blue-Green Deployment
Running Jobs and CronJobs
• Creating a Job and CronJob
Performing Monitoring and Troubleshooting Tasks
• Utilizing troubleshooting techniques with kubectl
Module 4: Automation & Operational Efficiency
Automating Common Kubernetes Tasks Using Python
• Performing administrative operations in Kubernetes via Python
• Defining Configuration objects with Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events via Python
• Scaling Deployments using Python
Understanding the Challenges of Automating Deployments
• Utilizing Declarative Configuration in Kubernetes
• Maintaining Configuration Integrity
Adopting the GitOps Approach for Automated Deployments
• Core GitOps Principles
• Introducing Flux
• Installing Flux into a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Leveraging Notifications
• Structuring the Source Repository
Managing Application Updates with Image Automation
• Updating an Application Deployment via 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
• Examining Pod and Container Logs
• Reviewing Control Plane Logs
• Monitoring Resource Usage of Nodes and Pods
Collecting and Analyzing the Logs
• Log Aggregation Techniques
• Log Visualization Methods
Distributed Tracing in Kubernetes
• Defining Distributed Tracing
• Utilizing OpenTelemetry
• Employing Distributed Tracing Tools
• Instrumenting an Application
• Using Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Observability Concepts
• Overview of 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
• Identifying various types of failures in a cluster environment
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Addressing Network Issues
‥ DNS Failures and Application Timeout Handling
• Simulating an API Server Outage
• Stress Testing System Stability with High Traffic
• Managing Storage Failures
• Resolving 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
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Conducting Cluster Analysis with K8sGPT
• Diagnosing Real-Time Issues via 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 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
