Google Cloud for AWS Professionals

Updated March 16, 2020

This guide is designed to equip professionals who are familiar with Amazon Web Services (AWS) with the key concepts required to get started with Google Cloud. The guide compares Google Cloud with AWS and highlights the similarities and differences between the two. In addition, the guide provides quick-reference mappings of AWS products, concepts, and terminology to the corresponding products, concepts, and terminology on Google Cloud.

Why Google Cloud?

For over 20 years, Google has been building one of the fastest, most powerful, and highest-quality cloud infrastructures on the planet. Internally, Google uses this infrastructure for several high-traffic and global-scale services, including Gmail, Maps, YouTube, and Search. Because of the size and scale of these services, Google has put a lot of work into optimizing its infrastructure and creating a suite of tools and services to manage it effectively. Google Cloud puts this infrastructure and these management resources at your fingertips.

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Regions and zones

Nearly all AWS products are deployed within regions located around the world. Each region comprises a group of data centers that are in relatively close proximity to each other. Amazon divides regions into availability zones. Similarly, Google Cloud divides its service availability into regions and zones that are located around the world. For a full mapping of Google Cloud global regions and zones, see Cloud Locations.

In addition, some Google Cloud services are located at a multi-regional level rather than the more granular regional or zonal levels. These services include Google App Engine and Google Cloud Storage. Currently, the available multi-regional locations are United States, Europe, and Asia.

By design, each AWS region is isolated and independent from other AWS regions. This design helps ensure that the availability of one region doesn't affect the availability of other regions, and that services within regions remain independent of each other. Similarly, Google Cloud regions are isolated from each other for availability reasons. However, Google Cloud has built-in functionality that enables regions to synchronize data across regions according to the needs of a given Google Cloud service.

AWS and Google Cloud both have points of presence (POPs) located in many more locations around the world. These POP locations help cache content closer to end users. However, each platform uses their respective POP locations in different ways:

AWS uses POPs to provide a content delivery network (CDN) service, CloudFront. CloudFront provides edge caching for services like Amazon S3 Transfer Acceleration and Lambda@Edge.
Google Cloud uses POPs to provide Google Cloud CDN (Cloud CDN) and to deliver built-in edge caching for services such as Google App Engine and Google Cloud Storage.

Google Cloud POPs connect to data centers through Google-owned fiber. This unimpeded connection means that Google Cloud-based applications have fast, reliable access to all of the services on Google Cloud.

To summarize, AWS location terms and concepts map to those of Google Cloud as follows:

Concept	AWS term	Google Cloud term
Cluster of data centers and services	Region	Region
Abstracted data center	Availability Zone	Zone
Edge caching	POP (services built on CloudFront)	POP (multiple services)

Accounts, limits, and pricing

To use an AWS service, you must sign up for an AWS account. After you have completed this process, you can launch any service under your account within Amazon's stated limits, and these services are billed to your specific account. If needed, you can create billing accounts, and then create sub-accounts that roll up to them. In this way, organizations can emulate a standard organizational billing structure.

Similarly, Google Cloud requires you to set up a Google account to use its services. However, Google Cloud groups your service usage by project rather than by account. In this model, you can create multiple, wholly separate projects under the same account. In an organizational setting, this model can be advantageous, allowing you to create project spaces for separate divisions or groups within your company. This model can also be useful for testing purposes: once you're done with a project, you can delete the project, and all of the resources created by that project will be deleted as well.

AWS and Google Cloud both have default soft limits on their services for new accounts. These soft limits are not tied to technical limitations for a given service—instead, they are in place to help prevent fraudulent accounts from using excessive resources, and to limit risk for new users, keeping them from spending more than intended as they explore the platform. If you find that your application has outgrown these limits, AWS and Google Cloud provide straightforward ways to get in touch with the appropriate internal teams to raise the limits on their services.

Because pricing tends to change more often than core features or services, this set of articles will avoid pricing specifics where possible. However, each article will discuss the pricing model behind each service wherever helpful. For up-to-date price comparisons for your specific solution, use the Amazon pricing calculator and Google Cloud calculator to see which configuration provides the best value in terms of flexibility, scalability, and cost.

Web consoles and command-line interfaces

AWS and Google Cloud both provide web-based consoles. Each console allows users to create, manage, and monitor their resources. The console for Google Cloud is located at https://console.cloud.google.com/.

AWS and Google Cloud also provide a command-line interface (CLI) for interacting with the services and resources. AWS provides the Amazon CLI, and Google Cloud provides the Cloud SDK. Each is a unified CLI for all services, and each is cross-platform, with binaries available for Windows, Linux, and macOS. In addition, in Google Cloud, you can use the Cloud SDK in your web browser by using Google Cloud Shell. Cloud Shell also provides pre-installed utilities and pre-set environmental variables.

Service types

At a high level, cloud platforms begin by providing a set of baseline services: compute, storage, networking, and database services. AWS baseline services include:

Compute: Amazon Elastic Compute Cloud (EC2)
Storage: Amazon Simple Storage Service (S3) and Amazon Elastic Block Store (EBS)
Networking: Amazon Virtual Private Cloud (VPC)
Databases: Amazon Relational Database Service (RDS) and Amazon DynamoDB

Google Cloud baseline services include:

Compute: Google Compute Engine and Google App Engine
Storage: Google Cloud Storage and Google Persistent Disk
Networking: Google Virtual Private Cloud
Databases: Google Cloud SQL, Google Firestore, and Google Cloud Bigtable

Each platform then builds other higher-level services on top of these services. Typically, these higher-level services can be categorized as one of five types:

Application services: Services designed to help optimize applications in the cloud. Examples include Amazon SNS and Google Pub/Sub.
Big data and analytics services: Services designed to help process large amounts of data, such as Amazon Kinesis and Google Dataflow.
Machine learning services: Services designed to help you incorporate perceptual AI such as image or speech recognition, or to train and deploy your own machine learning models. Examples include Amazon SageMaker and Google AI Platform.
Operations services: Services designed to help you track the performance of an application. Examples include Amazon CloudWatch and Google Cloud Monitoring.
Security services: Services designed to keep your Google Cloud resources secure. Examples include AWS Shield, AWS Identity and Access Management (IAM), Google Cloud Armor, and Identity and Access Management (IAM) .

Service comparisons

The following table provides a side-by-side comparison of the various services available on AWS and Google Cloud.

Service Category	Service	AWS	Google Cloud
Compute	IaaS	Amazon Elastic Compute Cloud	Compute Engine
	PaaS	AWS Elastic Beanstalk	App Engine
	FaaS	AWS Lambda	Cloud Functions
Containers	CaaS	Amazon Elastic Kubernetes Service, Amazon Elastic Container Service	Google Kubernetes Engine
	Containers without infrastructure	AWS Fargate	Cloud Run
	Container registry	Amazon Elastic Container Registry	Container Registry
Networking	Virtual networks	Amazon Virtual Private Cloud	Virtual Private Cloud
	Load balancer	Elastic Load Balancer	Cloud Load Balancing
	Dedicated Interconnect connection	AWS Direct Connect	Cloud Interconnect
	Domains and DNS	Amazon Route 53	Google Domains, Cloud DNS
	CDN	Amazon CloudFront	Cloud CDN
	DDoS firewall	AWS Shield, AWS WAF	Google Cloud Armor
Storage	Object storage	Amazon Simple Storage Service	Cloud Storage
	Block storage	Amazon Elastic Block Store	Persistent Disk
	Reduced-availability storage	Amazon S3 Standard-Infrequent Access, Amazon S3 One Zone-Infrequent Access	Cloud Storage Nearline and Cloud Storage Coldline
	Archival storage	Amazon Glacier	Cloud Storage Archive
	File storage	Amazon Elastic File System	Filestore
	In-memory data store	Amazon ElastiCache for Redis	Memorystore
Database	RDBMS	Amazon Relational Database Service, Amazon Aurora	Cloud SQL, Cloud Spanner
	Document data storage	Amazon DocumentDB	Firestore
	In-memory data storage	Amazon ElastiCache	Memorystore
	NoSQL: Key-value	Amazon DynamoDB	Firestore (document workloads), Cloud Bigtable (key/value workloads), Cloud Spanner (scale-out relational workloads)
	NoSQL: Indexed	Amazon SimpleDB	Firestore
	SQL	Amazon Aurora	Cloud Spanner
Data analytics	Data warehouse	Amazon Redshift	BigQuery
	Query service	Amazon Athena	BigQuery
	Messaging	Amazon Simple Notification Service, Amazon Simple Queueing Service	Pub/Sub
	Batch data processing	Amazon Elastic MapReduce, AWS Batch	Dataproc, Dataflow
	Stream data processing	Amazon Kinesis	Dataflow
	Stream data ingest	Amazon Kinesis	Pub/Sub
	Workflow orchestration	Amazon Data Pipeline, AWS Glue	Cloud Composer
Management tools	Deployment	AWS CloudFormation	Cloud Deployment Manager
	Cost management	AWS Budgets	Cost Management
Operations	Monitoring	Amazon CloudWatch	Cloud Monitoring
	Logging	Amazon CloudWatch Logs	Cloud Logging
	Audit logging	AWS CloudTrails	Cloud Audit Logs
	Debugging	AWS X-Ray	Cloud Debugger
	Performance tracing	AWS X-Ray	Cloud Trace
Security & identity	IAM	Amazon Identity and Access Management	Identity and Access Management
	Secret management	AWS Secrets Manager	Secret Manager
	Encrypted keys	AWS Key Management Service	Cloud Key Management Service
	Resource monitoring	AWS Config	Cloud Asset Inventory
	Vulnerability scanning	Amazon Inspector	Web Security Scanner
	Threat detection	Amazon GuardDuty	Event Threat Detection (beta)
	Microsoft Active Directory	AWS Directory Service	Managed Service for Microsoft Active Directory
Machine learning	Speech	Amazon Transcribe	Speech-to-Text
	Vision	Amazon Rekognition, Amazon Textract	Cloud Vision
	Natural Language Processing	Amazon Comprehend	Cloud Natural Language API
	Translation	Amazon Translate	Cloud Translation
	Conversational interface	Amazon Lex	Dialogflow Enterprise Edition
	Video intelligence	Amazon Rekognition Video	Video Intelligence API
	Auto-generated models	Amazon SageMaker Autopilot	AutoML
	Fully managed ML	Amazon SageMaker	AI Platform
Internet of Things	IoT services	Amazon IoT	Cloud IoT

What's next?

Check out the Google Cloud for AWS Professionals articles for each service type: