This course is intended for the following participants:
• Cloud Solutions Architects and 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
• Systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment
• Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs
• Creating and maintaining machine learning and statistical models
• Querying datasets, visualizing query results and creating reports
To get the most out of this course, you should have:
• Completed the Google Cloud Platform Fundamentals or have equivalent experience
• Basic proficiency with command-line tools and Linux operating system environments
This course is intended for the following participants:
• 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 Platform
• List the different ways of interacting with GCP
• Use the GCP Console and Cloud Shell
• Create Cloud Storage buckets
• Use the GCP Marketplace to deploy solutions
Module 2: Virtual Networks
• List the VPC objects in GCP
• Differentiate between the different types of VPC networks
• Implement VPC networks and firewall rules
• Design a maintenance server
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: Cloud IAM
• 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
• Recognise how quotas protect GCP customers
• Use labels to organise resources
• Explain the behaviour of budget alerts in GCP
• Examine billing data with BigQuery
Module 7: Resource Monitoring
• Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging
• Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring
• Use Stackdriver Debugger to identify and fix errors
Module 8: Interconnecting Networks
• Recall the GCP interconnect and peering services available to connect your infrastructure to GCP
• Determine which GCP 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 GCP load balancer to use in specific circumstances
• Describe autoscaling behaviour
• Configure load balancers and autoscaling
Module 10: Infrastructure Automation
• Automate the deployment of GCP services using Deployment Manager or Terraform
• Outline the GCP Marketplace
Module 11: Managed Services
• Describe the managed services for data processing in GCP
Click on the following link to see the current Course Schedule
Our minimum class-size is 3 for this course.
If there are no scheduled dates for this course, it can be customized to suit the time and skill needs of clients and it can be held online, at a rented location or at your premises.
Click on the following link below to arrange for a custom course: Enquire about a course date
Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. The service allows users to launch large compute clusters on Google’s infrastructure. The Google Compute Engine API provides users with an interface for interacting with their resources. It is a customizable compute service that lets you create and run virtual machines on Google’s infrastructure. You can create a Virtual Machine (VM) that fits your needs in the cost-effective, secure cloud environment which spans 23 Google Cloud regions.
You might use Compute Engine when you:
• Are migrating existing applications through lift-and-shift or lift-and-modernize approaches to kick off your infrastructure modernization journey.
• Run Windows or other 3rd party applications where you are bringing your own license (BYOL) to CGP or use a license-included VM image. These include ones available in the GCP Marketplace.
• Have highly customized business logic or you want to run your own storage system.
This service offers the choice of preset or custom machine sizes that most closely resemble your on-premise structure to best support your workloads. A partner will do this by rightsizing your environment with recommendations for the machine sizes that work best with your instance types and managed instances groups.
Google Cloud offers a wide range of Compute Engine machine types. These include:
• General-purpose N1, N2, N2D and E2 machines that offer the best price-performance ratio.
• Memory-optimized VMs that offer higher memory per core, up to 12 TB.
• Compute-optimized machines that offer the highest performance per core for compute-intensive workloads.
• Shared-core machines for N1 and E2 VMs for a cost-effective way to run small, non-resource-intensive applications.
You also can use what are known as preemptible VMs. These are low-cost, short-term instances that are ideal for running batch jobs and fault-tolerant workloads. Google Cloud indicates that these VMs cut budgets up to 80% over traditional VMs but offer the same performance and capabilities for short-term use.
Additional Information and FAQs
CERTFICATE OF COMPLETION: Participants will receive a certificate of completion at the end of a course. This is not an official certification for the product and/or software. Our courses do indicate the appropriate certification exam(s) that the participant can sit. Data Vision Systems does not provide certification or deliver the certification exams. Participants are responsible for arranging and paying for the certification exams on the appropriate certification body.
CANCELLATION POLICY: There is never a fee for cancelling seven business days before a class for any reason. Data Vision Systems reserves the right to cancel any course due to insufficient registration or other extenuating circumstances. Participants will be advised prior to doing so.