Google Cloud Fundamentals for AWS Professionals


Course Number: GCP-104

Duration: 1 day (6.5 hours)

Format: Live, hands-on

Google Cloud Fundamentals Training Overview

This Google Cloud Fundamentals for AWS Professionals training course teaches AWS professionals how to configure Google Cloud products immediately. This GCP course covers the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database.

Location and Pricing

This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.

In addition, some Cloud courses are available as live, online classes for individuals.

Objectives

  • Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake
  • Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more
  • Manage and monitor applications
  • Explain feature and pricing model differences

Prerequisites

All attendees must have:

  • Basic proficiency with networking technologies like subnets and routing
  • Basic proficiency with command-line tools
  • Experience with Amazon VPC, Amazon EC2 instances, and disks.

Familiarity with Amazon S3 and AWS database technologies is recommended.

Outline

Expand All | Collapse All

Introducing Google Cloud
  • Advantages of Google Cloud
  • Components of Google's network infrastructure, including Points of presence, data centers, regions, and zones
  • The difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS)
Getting Started with Google Cloud
  • Projects on Google Cloud Platform
  • How AWS’s resource hierarchy differs from Google Cloud’s
  • Use cases for Identity and Access Management
  • How AWS IAM differs from Google Cloud IAM
  • Methods of interacting with Google Cloud Platform
  • Launch a solution using Cloud Marketplace
Virtual Machines in the Cloud
  • Use cases for Google Compute Engine
  • Networking in Google Cloud Platform
  • How Amazon VPC differs from Google VPC
  • Similarities and differences between Amazon EC2 and Google Compute Engine
  • Approaches to load-balancing in Google
  • Cloud differs from those in AWS
  • Deploy applications using Google Compute Engine
Storage in the Cloud
  • Use cases for Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore
  • How Amazon S3 and Amazon Glacier compare to Cloud Storage
  • Compare Google Cloud’s managed database services with Amazon RDS and Amazon Aurora
  • Storage options on Google Cloud Platform
  • Load data from Cloud Storage into BigQuery
  • Perform a query on the data in BigQuery
Containers in the Cloud
  • Define the concept of a container and identify uses for containers
  • Use cases for Google Container Engine and Kubernetes
  • How Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE
  • Provision a Kubernetes cluster using Kubernetes Engine
  • Deploy and manage Docker containers using kubectl
Applications in the Cloud
  • Use cases for Google App Engine
  • Contrast the App Engine Standard environment with the App Engine Flexible environment
  • How App Engine differs from Amazon Elastic Beanstalk
  • Use cases for Google Cloud Endpoints
Developing, Deploying and Monitoring in the Cloud
  • Options for software developers to host their source code
  • Template-based creation and management of resources
  • How Cloud Deployment Manager differs from AWS CloudFormation
  • Integrated monitoring, alerting, and debugging
  • How Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail
  • Create a Deployment Manager deployment
  • Update a Deployment Manager deployment
  • View the load on a VM instance using Google Monitoring.
Big Data and Machine Learning in the Cloud
  • Use cases for the products and services in the Google Cloud big data and machine learning platforms
  • How Google Cloud BigQuery differs from AWS Data Lake
  • How Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus
  • How Google Cloud’s machine-learning APIs differ from AWS's
  • Load data into BigQuery from Cloud Storage
  • Queries using BigQuery to gain insight into data
Conclusion
  • Review the products that make up Google Cloud and remember how to choose among them
  • Next steps for training and certification
  • The process of migrating from AWS to Google Cloud

Training Materials:

All GCP training students receive comprehensive courseware.

Software Requirements:

Students must have a modern web browser (ideally Chrome) and Internet access.



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