GCP for AWS Professionals - Learn Google Cloud [2021]

Map aws compute and networking to Google Cloud by outlining Compute Engine, Persistent Disk, Cloud Load Balancing, App Engine, Google Kubernetes Engine, Cloud Functions, virtual private cloud, vpn, and interconnect.

Describe AWS and GCP storage types: object storage with S3 and cloud storage, block storage with EBS and persistent disk, and file storage with EFS and file store.

Explore relational and NoSQL database options across AWS and GCP, including Cloud SQL, Cloud Spanner, BigQuery, and Bigtable. Learn caching with Memorystore and ElastiCache and data migration.

Explore Google Cloud equivalents for AWS services, including Cloud Pub/Sub, Cloud IAM, Cloud KMS, and Cloud Deployment Manager, plus Cloud Monitoring, Cloud Logging, and Cloud Trace.

Explore how regions and zones drive high availability, low latency, and regulatory compliance in AWS and GCP, with multi-region and dual-region options for cloud storage buckets.

Explore Google Compute Engine basics, including creating virtual machines, choosing images and machine types, configuring firewall rules, and attaching boot and persistent disks for scalable workloads.

Learn to install and configure Apache 2 http server on a Google Compute Engine vm, and customize the index page with host name and ip address using shell commands.

Learn how Google Cloud assigns internal (private) IPs and ephemeral external IPs to Compute Engine VMs, and how to reserve, attach, detach, and release static external IP addresses.

Explore how to manage Google Compute Engine virtual machines using instance templates, managed and unmanaged instance groups with autoscaling, sole tenant nodes, and VM manager for patch management.

Use a startup script to bootstrap a Compute Engine VM, automatically install Apache 2, apply OS patches, and serve a simple page.

Discover how instance templates streamline VM creation in Google Cloud by predefining machine type, image, startup script, and firewall settings, then launch multiple VMs from a single template.

Reduce vm boot time by creating a custom image with pre-installed patches and software, then deploy new instances from that image using an instance template.

Optimize Google Compute Engine costs with preemptible VMs, committed use discounts, and sustained use discounts, and set up budgets and alerts to monitor spend.

Explore Google Cloud Compute Engine VM options, including region, zone, confidential VM, container-optimized images, and service accounts for IAM access. Configure networking, disks, and instance groups with autoscaling features.

Learn how to manage virtual machines with instance groups in GCP, including managed (MIG) vs unmanaged groups, autoscaling, autohealing, rolling updates, canary deployments, templates, and regional versus zonal deployment.

Explore creating managed instance groups in Google Cloud, using instance templates, autoscaling from CPU utilization or Stackdriver metrics, cool down period, and health checks for autohealing across zones.

Manage a Google Cloud managed instance group with autoscaling and autohealing, using health checks to keep two instances healthy across two zones, and adjust minimum and maximum as needed.

Compare AWS elastic load balancer with cloud load balancing in GCP, explaining traffic distribution across VM instances in multiple regions using a single anycast IP.

Configure internet-facing http load balancer in Google Cloud to distribute traffic to a managed instance group. Define a backend service, health checks, host and path rules, and a frontend.

Learn how a Google Cloud load balancer distributes traffic across a managed instance group using a backend service, with health checks, monitoring, and a front end port.

Review Google Cloud Platform compute options, including Compute Engine, App Engine, Kubernetes Engine, container as a service, and Cloud Functions, while comparing AWS Elastic Beanstalk, EKS, ECS, Fargate, and Lambda.

Discover how to deploy and scale web applications with App Engine, compare it to AWS Elastic Beanstalk, and manage releases with canary, blue-green, and traffic splitting.

Understand app engine standard and flexible environments: standard uses language-specific sandboxes with v1/v2 and Python/PHP restrictions, while flexible runs docker containers on compute engine vms with runtime support.

Explore the App Engine component hierarchy: application, service, and versions. Learn how a single application hosts multiple services and versions with traffic split, instances, and rollback capabilities.

Create a new project, enable the App Engine API, pick a region, and deploy services with gcloud app deploy, using app.yaml and python39.

Explore the App Engine dashboard to monitor versions, services, traffic, and errors, and learn how deploying V2 auto-switches traffic from V1 while both run.

Deploy a new App Engine version without promoting traffic, then gradually split traffic between V2 and V3 by IP, random, or cookie until V3 is fully live.

Learn to create multiple App Engine services and versions, deploy with gcloud app deploy using a named service, and manage traffic and URLs via the console and CLI.

Compare App Engine with AWS Elastic Beanstalk, detailing use cases for simple web apps, batch programs, microservices, container deployments, Firestore and Cloud SQL databases, queues, and deployment strategies.

Master container orchestration with Google Kubernetes Engine, deploying self-contained Docker images for microservices and using autoscaling, load balancing, zero-downtime deployments, and integrated cloud logging and monitoring.

Kick off your Kubernetes journey by creating a GKE cluster with a default node pool, enable the Kubernetes Engine API, and deploy a microservice using gcloud container clusters create.

Connect to the cluster, get credentials with gcloud, deploy a microservice with kubectl, and expose it via a load balancer to obtain an external IP.

Explore managing a Kubernetes cluster in Google Cloud Console, including node pools, deployments, services, load balancers, and ingress routing for microservices.

Scale deployments to run multiple instances and observe load balancing across pods; resize the node pool to adjust cluster capacity, illustrating Kubernetes autoscaling.

Learn to autoscale both Kubernetes deployments and clusters using horizontal pod autoscalers and gcloud updates, and configure microservices with ConfigMaps and Secrets for secure, centralized settings.

Learn how to deploy Kubernetes with yaml declarative configuration and compare it to imperative commands, edit deployment yaml to set replicas, and apply changes with kubectl apply -f.

End the Kubernetes journey by deploying a microservice that needs a GPU using a new node pool, configuring a node selector, and deleting resources with kubectl and gcloud.

Learn serverless with Google Cloud Functions, compare to AWS Lambda, paying per invocation and memory while the cloud handles scaling and separate instances.

Create and deploy your first Google Cloud Function in a selected project using HTTP triggers, with unauthenticated access, then enable Cloud Build, run tests, and review Cloud Logging.

Compare AWS Lambda and Google Cloud Functions, detailing memory options, runtime limits, pricing increments, and how triggers, HTTP exposure, API gateway, and logging differ.

Cloud Run deploys containerized apps to production in seconds as a fully managed serverless platform. Explore revisions, metrics, logs, YAML deployment, Cloud Run for Anthos for multi-cloud and on-premise use.

Learn to manage encryption keys in Google Cloud with Cloud KMS, including symmetric and asymmetric keys, and options for Google-managed, customer-managed, or bring-your-own-key.

Enable the cloud KMS APIs, create a key ring, generate a symmetric key, and use it to encrypt a VM disk with a customer managed key and service account permissions.

Take a tour of GCP storage: persistent disk for block storage, local ssd for ephemeral blocks, and cloud storage with nearline, coldline, archive classes—compared to AWS S3, EBS, and Glacier.

Compare block storage options in GCP with AWS, detailing local SSDs as ephemeral storage and persistent disks, their sizing, and differential snapshots with zonal and regional availability.

Learn to manage Google Cloud persistent disks and snapshots: create and attach boot disks, preserve disks on deletion, create and use snapshots, and schedule automated snapshots for VM stability.

Learn how machine images differ from images, and how to create and use machine images to deploy VM instances with boot and data disks, configuration, metadata, and permissions.

Explore Google Cloud Filestore, a high-performance NFS v3 file storage up to 320 TB with HDD or SSD, for file shares and content management, attachable to VMs or Kubernetes.

Learn Google Cloud Storage, the object storage service, with buckets in a project, region options (region, multi-region, dual-region) and storage classes standard, nearline, coldline, archive, offering 11 nines durability.

Explore creating a Google Cloud Storage bucket, choose a unique name, select region or multi-region options, and apply storage classes from standard to archive while uploading objects.

Automate cost savings with object lifecycle management by defining conditions—age, created time, live status, storage class, and newer versions—that trigger automatic moves or deletions.

Explore cloud identity and access management, distinguishing authentication from authorization for human and non-human identities. Learn how AWS IAM and Google Cloud IAM control access to servers and databases.

Review essential aws iam concepts, including users, groups, roles, and policies, and how management console access, programmatic access, and cross-account permissions enable secure resource access.

Explore Google Cloud IAM, where roles define permissions and policies bind these roles to members for specific resources, such as storage buckets, with steps to create and assign.

Explore Google Cloud IAM roles by examining basic, predefined, and custom roles, and learn how permissions define access across resources like Cloud Storage and App Engine.

Discover how service accounts let a VM access cloud storage using an email-like service account ID and RSA keys, with default, user managed, and Google managed types and granular permissions.

Explore service accounts in Google Cloud by creating a Compute Engine service account, granting storage admin permissions, launching a VM, and using gsutil to create a cloud storage bucket.

Compare IAM in GCP with AWS, highlighting authentication versus authorization, service accounts for VMs, audit logging, and GCP policy as bindings mapping roles to members.