Course Outline
Module 1: Microservices Design
• Establishing effective Microservice Boundaries
• Implementing Domain Driven Design (DDD)
• Alternative Boundary Strategies (Volatility, Data, Technology, Organizational)
• Decomposing the Monolith
• Pitfalls of Premature Decomposition
• Layer-Based Decomposition Approaches
• Applying Decomposition Patterns (Strangler Fig, Parallel Run, Feature Toggle)
• Addressing Data Decomposition Concerns (Performance, Integrity, Transactions)
Module 2: Optimizing Docker and the Runtime
• Selecting appropriate base images
• Reducing layer counts
• Leveraging multi-stage builds
• Image optimization techniques (e.g., ordering multi-line arguments)
• Maximizing build cache efficiency
• Fixing specific image versions
• Refining resource allocation settings
• Adhering to secure container practices
• Configuring runtimes for peak performance
Module 3: Kubernetes & Release Strategies
Kubernetes Deployments Overview
• Executing Initial Deployments
• Exploring Kubernetes Deployment Options
Executing Rolling Update Deployments
• Comprehending Rolling Updates
• Creating and Implementing a Rolling Update
• Reverting Deployment Changes
Executing Canary Deployments
• Understanding Canary Releases
• Creating and Implementing a Canary Deployment
Executing Blue-Green Deployments
• Understanding Blue-Green Strategy
• Creating and Implementing a Blue-Green Deployment
Managing Jobs and CronJobs
• Setting up Jobs and CronJobs
Conducting Monitoring and Troubleshooting Activities
• Troubleshooting Methods using kubectl
Module 4: Automation & Operational Efficiency
Automating Kubernetes Tasks with Python
• Automating administrative operations via Python
• Defining Configuration objects with Python
• Creating Deployment objects using Python
• Monitoring Kubernetes Events through Python scripts
• Scaling Deployments programmatically
Understanding Challenges in Automating Deployments
• Declarative Configuration in Kubernetes
• Ensuring Configuration Integrity
Adopting GitOps for Deployment Automation
• Core GitOps Principles
• Introducing Flux
• Deploying Flux to a Kubernetes Cluster
Configuring Flux for Automated Workflows
• Utilizing Notification Systems
• Structuring Source Repositories
Managing Application Updates with Image Automation
• Updating Applications via Flux
• Scanning Container Registries for Tags
• Defining Policies for Latest Image Selection
• Configuring Flux to Automate 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
• Assessing Node and Pod Resource Usage
Collecting and Analyzing Logs
• Log Aggregation Techniques
• Log Visualization Tools
Distributed Tracing in Kubernetes
• Defining Distributed Tracing
• Utilizing OpenTelemetry
• Overview of Distributed Tracing Tools
• Instrumenting Applications for Tracing
• Identifying Performance Issues via Tracing
Monitoring with Prometheus and Grafana
• Key Observability Concepts
• Essential Monitoring Tools
• Implementing Prometheus Instrumentation
Advanced Logging Use Cases
• Log Processing Strategies
• Filtering and Enriching Logs
• Event Sourcing Techniques
Module 6: Cluster Crisis Simulation & Incident Response
• Recognizing various failure types in cluster environments
• Simulating Node Failures
• Scenarios for Pod Eviction and Resource Exhaustion
• Network Disruptions
• Handling Application Timeouts due to DNS Failures
• Simulating API Server Outages
• Testing System Stability Under High Traffic
• Storage Failures
• 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.)
• Cluster Analysis with K8sGPT
• Diagnosing Real-Time Issues using K8sGPT
• Deploying the In-Cluster Operator for K8sGPT
Requirements
- Fundamental knowledge of Linux command-line operations
- Practical experience in application development or system administration
- Understanding of container technologies (Docker concepts)
- Basic comprehension of Kubernetes fundamentals (pods, deployments, services)
- General awareness of software architecture patterns (e.g., APIs, microservices)
Target Audience:
- DevOps Engineers
- Site Reliability Engineers (SREs)
- Backend / Software Developers engaged with microservices
- Cloud Engineers and Platform Engineers
-
System Administrators moving towards Kubernetes-based infrastructures
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
