Google Cloud Professional
Course Content- Duration 40hrs
1. INTRODUCTION TO GOOGLE CLOUD
Understanding the fundamentals of Google Cloud Platform
The Google global infrastructure
Products for storage, compute, networking, Machine Learning, and more
Different projects running on the GCP infrastructure, including Google projects
2. GOOGLE CLOUD SERVICES
Introduction to Google Cloud services
Managing Google services using command-line tools, app, and console
Installing and configuring SDK
Deploying Cloud Shell for GCP environment management
Create 2 VMs in differents , Deploy Nginx and establish connection between the 2 vms
3. I AM & SECURITY SERVICES
Different security and identity and access management (IAM) services
Various roles in IAM
Creating and managing Google resource permissions
The creation of custom roles
Sharing resources and isolation
Deploying penetration testing, auditing, and security controls
Create a Custom Roles through console.
Use the Command Line (glcoud) to create a custom role by
combining one or more of the available Cloud IAM permissions.
4. GOOGLE NETWORKING SERVICES
Introduction to Google networking
Setting up the Google network
Connecting various Google Cloud Platform resources
Isolation using firewalls and network policies
Creating and managing virtual private network
Working with cloud routers and interconnecting networks
First you cannot create VM instances without a VPC network.
Next you create a new auto mode VPC network with subnets, routes, firewall rules,
and two VM instances and tested the connectivity for the VM instances. Because
auto mode networks aren't recommended for production, you convert the auto mode
network to a custom mode network.
Next, you create two more custom mode VPC networks with firewall rules and VM
instances using the Cloud Console and the gcloud command line.
Then you test the connectivity across VPC networks and VMs, which work when
pinging external IP addresses but not when pinging internal IP addresses they do not
VPC networks are by default isolated private networking domains. Therefore, no
internal IP address communication is allowed between networks.
Project: VPC Network Peering
Google Cloud Virtual Private Cloud (VPC) Network Peering allows private
connectivity across two VPC networks regardless of whether or not they
belong to the same project or the same organization.
VPC Network Peering allows you to build SaaS (Software-as-a-Service)
ecosystems in Google Cloud, making services available privately across
different VPC networks within and across organizations, allowing workloads to
communicate in private space.
Project: Load Balancing:
Google Cloud HTTP(S) load balancing is implemented at the edge of Google's network in Google's points of presence (POP) around the world. User traffic directed to an HTTP(S) load balancer enters the POP closest to the user and is then load balanced over Google's global network to the closest backend that has sufficient capacity available.
In this lab, you configure an HTTP Load Balancer with global backends, as shown in the diagram below. Then, you stress test the Load Balancer.
5. GOOGLE COMPUTING SERVICES
Understanding Google computing services
Creating and managing virtual machines in Google Cloud
Launching VMs on-demand using Google Compute Engine
Choosing the right computing solutions based on the workload like memory or CPU
Important actions with Compute Engine
Balancing the load
Project : App Engine:
Nodejs on App Engine
1. Deploy a nodejs application on App Engine standard using gcloud (use default application
mentioned in the gcp tutorial)
2. Deploy the above version as ver-1
3. Change the content of html to “Hello world from intellipaat”
4. Deploy the above as ver-2
5. Browse both ver-1 and ver-2 using the URL
6. Finally deploy a test instance, and load test on your instance with 5000 requests (do this using
6. GOOGLE STORAGE SERVICES
Various storage services offered by Google Cloud
The process of storing and retrieving data
Rich content management
Hosting a website
Choosing the right storage options
Integrating on-premises with the cloud storage environment
Nearline and Coldline storage
Cloud Datastore (No SQL Database)
Managing cloud storage using Gsutil
7.Google Big Data
8.Google Artificial Intelligence
Cloud Vision API
Project: AI Project with various Components:
We will deploy a AI Application built on Python Flask web application to the App Engine Flexible environment.
The application allows a user to upload a photo of a person's face and learn how likely it is that the person is happy.
The application uses Python code, Google App Engine Flexible, Google Cloud APIs for Vision, Storage, and Datastore/Firestore.
9. MIGRATING TO GOOGLE CLOUD
Understanding how to migrate to Google Cloud
Various criteria to be considered
Choosing the right options as per the specific needs
Managing a hybrid cloud model
Complete migration from the on-premises model to the cloud
Choosing an automation framework for resource provisioning