Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Utilizing Domain Driven Design (DDD)
• Exploring Alternatives to Business Domain Boundaries (Volatility, Data, Technology, Organizational)
• Strategies for Splitting the Monolith
• The Pitfalls of Premature Decomposition
• Approaches to 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
• Minimizing the number of layers
• Leveraging multi-stage builds
• Techniques for Image optimization (sorting multi-line arguments, etc.)
• Maximizing the use of the build cache
• Pinning image versions for consistency
• Fine-tuning resource allocation
• Implementing secure container practices
• Configuring the runtime for peak 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
• Strategies for Rolling Back Deployment
Executing Canary Deployments
• Understanding Canary Deployments
• Creating and executing a Canary Deployment
Executing Blue-Green Deployments
• Understanding 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 using Python
• Watching Kubernetes Events via Python
• Scaling Deployments programmatically with Python
Understanding the Challenges of Automating Deployments
• Declarative Configuration with Kubernetes
• Ensuring the Integrity of Configuration
Adopting the GitOps Approach for Automating Deployments
• Core GitOps Principles
• Introduction to Flux
• Installing Flux to a Kubernetes Cluster
Configuring Flux for Automated Deployments
• Utilizing Notifications
• Structuring the Source Repository
Managing Application Updates via Image Automation
• Updating an Application Deployment using 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
• Examining Pod and Container Logs
• Reviewing Control Plane Logs
• Analyzing Resource Usage of Nodes and Pods
Collecting and Analyzing Logs
• Log Aggregation strategies
• Log Visualization techniques
Implementing Distributed Tracing in Kubernetes
• Understanding Distributed Tracing
• Leveraging OpenTelemetry
• Selecting Distributed Tracing Tools
• Instrumenting an Application
• Utilizing Tracing to Identify Performance Issues
Monitoring with Prometheus and Grafana
• Key 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
• Understanding different types of failures in a cluster environment
• Simulating Node Failures
• Scenarios involving Pod Eviction & Resource Exhaustion
• Addressing Network Issues
• Handling DNS failures and application timeouts
• Simulating an API Server Outage
• Stress-testing system stability with high traffic
• Addressing Storage Failure
• Identifying 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 the Cluster using K8sGPT
• Investigating Real-Time Issues with 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
