Architecting with Google Compute Engine Training in Greece

This course will familiarize you with Google Cloud's flexible infrastructure and platform services, with a specific focus on Compute Engine. This session uses a combination of lectures, demos, and hands-on labs to explore and deploy solution elements, including infrastructure components like networks, systems, and application services. You'll also learn how to deploy practical solutions such as secure interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.

Audience:

• Cloud Solutions Architects, DevOps Engineers.
• Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure, with a focus on Compute Engine.

• Have completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience.
• Have basic proficiency with command-line tools and Linux operating system environments.
• Have systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment.

• Configure VPC networks and virtual machines
• Administer Identity and Access Management for resources
• Implement data storage services in GCP
• Manage and examine billing of GCP resources
• Monitor resources using Stackdriver services
• Connect your infrastructure to GCP
• Configure load balancers and autoscaling for VM instances
• Automate the deployment of GCP infrastructure services
• Leverage managed services in GCP

Module 1: Introduction to Google Cloud

• List the different ways of interacting with Google Cloud.
• Use the Cloud Console and Cloud Shell.
• Create Cloud Storage buckets.
• Use the Google Cloud Marketplace to deploy solutions.

Module 2: Virtual Networks

• List the VPC objects in Google Cloud.
• Differentiate between the different types of VPC networks.
• Implement VPC networks and firewall rules.
• Implement Private Google Access and Cloud NAT.

Module 3: Virtual Machines

• Recall the CPU and memory options for virtual machines.
• Describe the disk options for virtual machines.
• Explain VM pricing and discounts.
• Use Compute Engine to create and customize VM instances.

Module 4: CloudIAM

• Describe the Cloud IAM resource hierarchy.
• Explain the different types of IAM roles.
• Recall the different types of IAM members.
• Implement access control for resources using Cloud IAM.

Module 5: Storage and Database Services

• Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable.
• Choose a data storage service based on your requirements.
• Implement data storage services.

Module 6: Resource Management

• Describe the cloud resource manager hierarchy.
• Recognize how quotas protect Google Cloud customers.
• Use labels to organize resources.
• Explain the behavior of budget alerts in Google Cloud.
• Examine billing data with BigQuery.

Module 7: Resource Monitoring

• Describe the services for monitoring, logging, error reporting, tracing, and debugging.
• Create charts, alerts, and uptime checks for resources with Cloud Monitoring.
• Use Cloud Debugger to identify and fix errors.

Module 8: Interconnecting Networks

• Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud.
• Determine which Google Cloud interconnect or peering service to use in specific circumstances.
• Create and configure VPN gateways.
• Recall when to use Shared VPC and when to use VPC Network Peering.

Module 9: Load Balancing and Autoscaling

• Recall the various load balancing services.
• Determine which Google Cloud load balancer to use in specific circumstances.
• Describe autoscaling behavior.
• Configure load balancers and autoscaling.

Module 10: Infrastructure Modernization

• Automate the deployment of Google Cloud services using Deployment Manager or Terraform.
• Outline the Google Cloud Marketplace.

Module 11: Managed Services

• Describe the managed services for data processing in Google Cloud.