Course Outline
Module 1: Microservices Design
• Defining effective Microservice Boundaries
• Utilizing Domain-Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• The Risks of Premature Decomposition
• Decomposition By Layer
• Applying 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 techniques (e.g., sorting multi-line arguments)
• Maximizing the use of the build cache
• Pinning image versions for stability
• Fine-tuning resource allocation
• Adhering to secure container practices
• Configuring the runtime for optimal 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 the Rolling Update mechanism
• Creating and executing a Rolling Update
• Performing Deployment Rollbacks
Implementing Canary Deployments
• Understanding the Canary Deployment strategy
• Creating and executing a Canary Deployment
Implementing Blue-Green Deployments
• Understanding the Blue-Green Deployment strategy
• Creating and executing a Blue-Green Deployment
Managing Jobs and CronJobs
• Creating a Job and CronJob
Conducting Monitoring and Troubleshooting Tasks
• Utilizing 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 using Python
• Monitoring Kubernetes Events via Python
• Scaling Deployments programmatically with Python
Addressing the Challenges of Automating Deployments
• Understanding Declarative Configuration with Kubernetes
• Ensuring the Integrity of Configuration
Adopting the GitOps Approach for Automating Deployments
• Core 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 via Image Automation
• Updating Application Deployments with Flux
• Scanning Container Image Repositories for new Tags
• Defining Policies for Latest Image selection
• Configuring Flux to perform Automatic Image Updates
Module 5: Observability & Root Cause Clarity
Kubernetes Logging and Tracing Capabilities
• The Importance of Logging and Tracing
• Accessing Kubernetes Logs
• Reviewing Pod and Container Logs
• Examining Control Plane Logs
• Monitoring Resource Usage of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation strategies
• Log Visualization techniques
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting an Application for Tracing
• Leveraging Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Core Observability Concepts
• Overview of Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Use Cases for Logging
• Processing Logs
• Filtering and Enriching Logs
• Implementing Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Understanding different types of failures in a cluster environment
• Simulating Node Failures
• Managing Pod Eviction & Resource Exhaustion Scenarios
• Addressing Network Issues
• Handling DNS failures and application timeout scenarios
• Simulating an API Server Outage
• Testing System Stability under High Traffic
• Managing Storage Failures
† Diagnosing Configuration Errors
• Understanding Incident Reporting Procedures
Module 7: AI To Support Troubleshooting
• Benefits of Generative AI for Kubernetes
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• Using K8sGPT Commands and Features
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing the Cluster using K8sGPT
• Investigating Real-Time Issues using K8sGPT
• Deploying the 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
