Build an effective data architecture, streamline data processing, and maintain large-scale data systems. In addition to Google Kubernetes Engine and multiple deployment approaches including how to: configure and build images to run and debug Docker containers, build Kubernetes Engine clusters, and manage them with
kubectl, deploy Kubernetes applications using deployments and continuous delivery techniques.
tools and techniques to help optimize resource usage and eliminate unnecessary costs on Google Kubernetes Engine (GKE): create and manage a multi tenant cluster, monitor resource usage by namespace, configure cluster and pod autoscaling, configure load balancing, and set up liveness and readiness probes.
Secure Workloads in Google Kubernetes Engine quest, where you learn about security at scale on Google Kubernetes Engine (GKE) including how to: migrate containers from virtual machines to Google Kubernetes Engine, restrict network connections in GKE using firewalls and Network Policies, use role-based access controls (RBAC) in GKE, use Binary Authorization for security controls of your images, secure applications in GKE using 3 access levels: host, network, Kubernetes API, and harden GKE cluster configurations.
Kubernetes Trainee Responsibilities:
- Week 1: Deploy to Kubernetes in Google Cloud
- Week 2: Optimize Costs for Google Kubernetes Engine.
- Week 3: Secure Workloads in Google Kubernetes Engine
- Week 4: Review of Submitted Assignments
- Bachelor’s degree holder or pursuing in Computer Science
- High level of accountability and motivation.
- Strong Interpersonal, time and project management, presentation, leadership, and communication skills.
- Creativity and ability to delegate responsibilities.
- Receptiveness to feedback and adaptability.
- Willingness to meet deadlines.