Course Outline
Module 1: Microservices Design
• A good Microservice Boundary
• Using Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Splitting the Monolith
• Premature decomposition
• Decomposition By Layer
• Using Decomposition Patterns (Strangler, Parallel Run, Feature Toggle)
• Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Choosing the right base image
• Minimizing the number of layers
• Using multi-stage builds
• Image optimization (sort multi-line arguments, etc.)
• Leveraging the build cache
• Pinning image versions
• Fine-tuning resource allocation
• Secure container practices
• Runtime configuration for performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview • Creating and executing an Initial Deployment • Kubernetes Deployment Options
Performing Rolling Update Deployments • Understanding Rolling Update • Creating and executing a Rolling Update • Rolling Back Deployment
Performing Canary Deployments • Understanding Canary Deployments • Creating and executing a Canary Deployment
Performing Blue-Green Deployments • Understanding Blue-Green Deployments • Creating and executing a Blue-Green Deployment
Running Jobs and CronJobs • Creating a Job and CronJob
Performing Monitoring and Troubleshooting Tasks • Troubleshooting Techniques with kubectl
Module 4: Automation & Operational Efficiency
Using Python to Automate Common Task in Kubernetes • Using Python to perform administrative operations in Kubernetes • Using Python to define Configuration objects • Using Python to create Deployment objects • Watching Kubernetes Events using Python • Scaling a Deployment using Python
Understanding the Challenges of Automating Deployments • Declarative Configuration with Kubernetes • Managing the Integrity of Configuration
Using the GitOps Approach for Automating Deployments • GitOps Principles • Introducing Flux • Installing Flux to a Kubernetes Cluster
Configuring Flux for Automated Deployments • Using Notifications • The Source Repository Structure
Handling Application Updates with Image Automation • Updating an Application Deployment with Flux • Scanning Container Image Repositories for Tags • Defining Policy for Latest Image selection • Configuring Flux to Perform Automatic Image Updates
Module 5: Observability & Root Cause Clarity
Kubernetes Logging and Tracing Capabilities • Why Are Logging and Tracing Important • Accessing the Kubernetes Logs • Pod and Container Logs • Control Plane Logs • Resource Usage of Nodes and Pods
Collecting and Analyzing the Logs • Log Aggregation • Log Visualization
Distributed Tracing in Kubernetes • What is distributed tracing • Using OpenTelemetry • Distributed Tracing Tools • Instrumenting an Application • Using Tracing to Find Performance Issues
Monitoring with Prometheus and Grafana • Observability concepts • Monitoring Tools • Using Prometheus Instrumentation
Advanced Uses Cases for Logging • Processing Logs • Filtering and Enriching the Logs • Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding the different types of failures in a cluster environment
• Simulating a Node Failures
• Pod Eviction & Resource Exhaustion Scenario
• Network Issues
• DNS failures to for application timeout handling
• Simulating an API Server Outage
• Simulating high traffic for system stability
• Storage Failure
• Configuration Errors
• Understanding Incident reporting procedures
Module 7: AI To support Troubleshooting
• Benefits of Generative AI for Kubernetes
• K8sGPT CLI architecture
• Install 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
- 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
