k8s advanced
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Harness Kubernetes’ extensibility to deploy modern patterns and learn to effectively handle production issues
Key Features
- Build and run efficient cloud-native applications on Kubernetes using industry best practices
- Operate Kubernetes in a production environment, troubleshoot clusters, and address security concerns
- Deploy cutting-edge Kubernetes patterns such as service mesh and serverless to your cluster
Description
Kubernetes is a modern cloud native container orchestration tool and one of the most popular open source projects worldwide. In addition to the technology being powerful and highly flexible, Kubernetes engineers are in high demand across the industry.
This course is a comprehensive guide to deploying, securing, and operating modern cloud native applications on Kubernetes. From the fundamentals to Kubernetes best practices, this training covers essential aspects of configuring applications. You’ll even explore real-world techniques for running clusters in production, tips for setting up observability for cluster resources, and valuable troubleshooting techniques. Finally, you’ll learn how to extend and customize Kubernetes, as well as gaining tips for deploying service meshes, serverless tooling, and more on your cluster.
By the end of this Kubernetes course, you’ll be equipped with the tools you need to confidently run and extend modern applications on Kubernetes.
What you will learn
- Set up Kubernetes and configure its authentication
- Deploy your applications to Kubernetes
- Configure and provide storage to Kubernetes applications
- Expose Kubernetes applications outside the cluster
- Control where and how applications are run on Kubernetes
- Set up observability for Kubernetes
- Build a continuous integration and continuous deployment (CI/CD) pipeline for Kubernetes
- Extend Kubernetes with service meshes, serverless, and more
Intended Audience
This Learning Path is intended specifically for Docker and Kubernetes application developers. Anyone interested in learning how to work with Kubernetes will also benefit from this Learning Path.
Prerequisites
A solid understanding of containers, and Docker in particular, will be of value. If you are not comfortable with Docker and Kubernetes , you are encouraged to complete the Docker and Kubernetes Learning Path.This Learning path helps you to learn from fundamentals to advanced Docker and Kubernetes running on Linux machines. You should be comfortable working with basic Linux commands.
Additional Documentation
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1Application Overview
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2Managing Configuration Files
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3Creating a Replicated Service Using Deployments
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4Best Practices for Image Management
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5Creating a Replicated Application
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6Setting Up an External Ingress for HTTP Traffic
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7Configuring an Application with ConfigMaps
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8Managing Authentication with Secrets
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9Deploying a Simple Stateful Database
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10Creating a TCP Load Balancer by Using Services
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11Using Ingress to Route Traffic to a Static File Server
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12Parameterizing Your Application by Using Helm
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13Deploying Services Best Practices
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14Goals
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15Building a Development Cluster
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16Setting Up a Shared Cluster for Multiple Developers
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17Onboarding Users
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18Creating and Securing a Namespace
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19Managing Namespaces
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20Cluster-Level Services
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21Enabling Developer Workflows
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22Initial Setup
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23Enabling Active Development
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24Enabling Testing and Debugging
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25Setting Up a Development Environment Best Practices
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26Metrics Versus Logs
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27Monitoring Techniques
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28Monitoring Patterns
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29Kubernetes Metrics Overview
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30cAdvisor
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31Metrics Server
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32kube-state-metrics
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33What Metrics Do I Monitor?
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34Monitoring Tools
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35Monitoring Kubernetes Using Prometheus
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36Logging Overview
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37Tools for Logging
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38Logging by Using an EFK Stack
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39Alerting
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40Best Practices for Monitoring, Logging, and Alerting
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41Monitoring
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42Logging
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43Alerting
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51Version Control
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52Continuous Integration
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53Testing
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54Container Builds
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55Container Image Tagging
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56Continuous Deployment
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57Deployment Strategies
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58Testing in Production
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59Setting Up a Pipeline and Performing a Chaos Experiment
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60Setting Up CI
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61Setting Up CD
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62Performing a Rolling Upgrade
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63A Simple Chaos Experiment
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64Best Practices for CI/CD
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70Distributing Your Image
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71Parameterizing Your Deployment
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72Load-Balancing Traffic Around the World
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73Reliably Rolling Out Software Around the World
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74Pre-Rollout Validation
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75Canary Region
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76Identifying Region Types
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77Constructing a Global Rollout
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78When Something Goes Wrong
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79Worldwide Rollout Best Practices
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80Kubernetes Scheduler
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81Predicates
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82Advanced Scheduling Techniques
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83Pod Affinity and Anti-Affinity
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84nodeSelector
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85Taints and Tolerations
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86Pod Resource Management
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87Resource Request
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88Resource Limits and Pod Quality of Service
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89PodDisruptionBudgets
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90Managing Resources by Using Namespaces
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91ResourceQuota
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92LimitRange
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93Cluster Scaling
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94Application Scaling
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95Scaling with HPA
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96HPA with Custom Metrics
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97Vertical Pod Autoscaler
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98Resource Management Best Practices
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99Kubernetes Network Principles
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100Network Plug-ins
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101Kubenet
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102Kubenet Best Practices
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103The CNI Plug-in
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104CNI Best Practices
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105Services in Kubernetes
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106Service Type ClusterIP
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107Service Type NodePort
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108Service Type ExternalName
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109Service Type LoadBalancer
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110Ingress and Ingress Controllers
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111Services and Ingress Controllers Best Practices
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112Network Security Policy
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113Network Policy Best Practices
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114Service Meshes
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115Service Mesh Best Practices
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116PodSecurityPolicy API
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117Enabling PodSecurityPolicy
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118Anatomy of a PodSecurityPolicy
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119PodSecurityPolicy Challenges
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120PodSecurityPolicy Best Practices
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121PodSecurityPolicy Next Steps
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122Workload Isolation and RuntimeClass
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123Using RuntimeClass
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124Runtime Implementations
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125Workload Isolation and RuntimeClass Best Practices
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126Other Pod and Container Security Considerations
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127Admission Controllers
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128Intrusion and Anomaly Detection Tooling
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129Why Policy and Governance Are Important
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130How Is This Policy Different?
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131Cloud-Native Policy Engine
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132Introducing Gatekeeper
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133Example Policies
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134Gatekeeper Terminology
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135Defining Constraint Templates
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136Defining Constraints
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137Data Replication
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138UX
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139Audit
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140Becoming Familiar with Gatekeeper
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141Gatekeeper Next Steps
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142Policy and Governance Best Practices
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151Importing Services into Kubernetes
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152Selector-Less Services for Stable IP Addresses
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153CNAME-Based Services for Stable DNS Names
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154Active Controller-Based Approaches
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155Exporting Services from Kubernetes
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156Exporting Services by Using Internal Load Balancers
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157Exporting Services on NodePorts
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158Integrating External Machines and Kubernetes
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159Sharing Services Between Kubernetes
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160Third-Party Tools
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161Connecting Cluster and External Services Best Practices
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162Why Is Kubernetes Great for Machine Learning?
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163Machine Learning Workflow
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164Machine Learning for Kubernetes Cluster Admins
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165Model Training on Kubernetes
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166Distributed Training on Kubernetes
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167Resource Constraints
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168Specialized Hardware
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169Libraries, Drivers, and Kernel Modules
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170Storage
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171Networking
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172Specialized Protocols
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173Data Scientist Concerns
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174Machine Leaning on Kubernetes Best Practices
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175Approaches to Developing Higher-Level Abstractions
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176Extending Kubernetes
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177Extending Kubernetes Clusters
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178Extending the Kubernetes User Experience
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179Design Considerations When Building Platforms
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180Support Exporting to a Container Image
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181Support Existing Mechanisms for Service and Service Discovery
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182Building Application Platforms Best Practices
Coming Soon