Course Outline
Module 1: Microservices Design
• Defining effective Microservice Boundaries
• Applying Domain Driven Design (DDD)
• Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• Pitfalls of Premature decomposition
• Decomposition By Layer
• Utilizing 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., handling multi-line arguments)
• Maximizing the build cache
• Pinning image versions for consistency
• Fine-tuning resource allocation
• Adhering to secure container practices
• Optimizing runtime configuration for performance
Module 3: Kubernetes & Release Strategies
Overview of Kubernetes Deployments
• Initiating and executing an Initial Deployment
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Understanding the mechanics of Rolling Updates
• Creating and executing a Rolling Update
• Performing Rolling Back Deployments
Executing Canary Deployments
• Grasping the concept of Canary Deployments
• Creating and executing a Canary Deployment
Executing Blue-Green Deployments
• Grasping the concept of Blue-Green Deployments
• Creating and executing a Blue-Green Deployment
Managing Jobs and CronJobs
• Creating a Job and CronJob
Conducting Monitoring and Troubleshooting Tasks
• Troubleshooting Techniques using 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 with Python
• Monitoring Kubernetes Events via Python
• Scaling Deployments programmatically with Python
Addressing Challenges in Automating Deployments
• Implementing Declarative Configuration with Kubernetes
• Ensuring the Integrity of Configuration
Adopting the GitOps Approach for Deployment Automation
• Core GitOps Principles
• Introduction to Flux
• Installing Flux onto 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 Image Repositories for Tags
• Establishing 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
• Pod and Container Logs
• Control Plane Logs
• Resource Usage Analysis for Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation Techniques
• Log Visualization Methods
Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Leveraging OpenTelemetry
• Exploring Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Utilizing 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
• Event Sourcing
Module 6: Cluster Crisis Simulation & Incident Response
• Recognizing various types of failures in a cluster environment
• Simulating Node Failures
• Pod Eviction & Resource Exhaustion Scenarios
• Network Issues
• DNS Failures and Application Timeout Handling
• 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
• Architecture of the K8sGPT CLI
• Installing the K8sGPT CLI
• K8sGPT Commands and Usage
• Utilizing K8sGPT Analyzers (podAnalyzer, pvcAnalyzer, rsAnalyzer, etc.)
• Analyzing Clusters using K8sGPT
• Diagnosing Real-Time Issues with 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 container concepts (Docker)
- Basic understanding of Kubernetes fundamentals (pods, deployments, services)
- General comprehension 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
