Google Cloud Cheat Sheet

Google Cloud Platform Cheat Sheet

One of the foremost players in the cloud service vendor market is Google Cloud Platform (GCP). The expertise of Google in running data centers is an undeniable fact, isn’t it? After all, it runs the world’s top search engine! After the launch of AWS in 2006, Google implemented its data center expertise for launching its own cloud service.

And today, it is one of the three prominent players among public cloud vendors alongside AWS and Microsoft Azure. The following discussion provides a Google Cloud cheat sheet for obtaining a basic understanding of Google Cloud Platform. The important elements in the discussion include an introduction of GCP along with its history and architecture.

Preparing to become a certified Google Cloud professional? Check our Google Cloud Certifications Training Courses now!

The discussion would also highlight the advantages of GCP and different approaches for working on Google Cloud Platform. Furthermore, the Google Cloud platform cheat sheet would provide brief outlines of important terms, services, products, and commands. In addition, the discussion would also illustrate the list of important certifications by Google Cloud Platform. 

Google Cloud Cheat Sheet: Terms, Definition, and Glossary 

As we start the Google Cloud Cheat Sheet, it is important to understand terms and definitions related to the Google Cloud Platform and cloud computing. Let’s understand some common terms, definition, and glossary.

Cloud Computing: The delivery of IT resources and services through a network rather than from on-premise resources.

Cloud Migration: The process of transferring applications, data, and services from on-premise systems to the cloud.

Cloud Service Provider (CSP): Any company which sells cloud computing services, either PaaS, IaaS or SaaS.

Container: A virtual instance with the facility of multiple isolated user-space instances allowed by the kernel of an operating system.

DevOps: A methodology derived from the combination of development and operations teams that promotes communication, collaboration and integration among them.

Google Cloud Platform: GCP is the cloud service offering of Google that provides Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) products.

Host Machine: The server or physical machine which stores containers or virtual machines.

Hybrid Cloud: Cloud computing environment developed with a combination of public and private clouds alongside on-premises solutions.

Instance: A single virtual machine or server supporting a particular workload.

Multi-tenancy: Model of software operation that facilitates running of multiple instances of one or more applications in a shared environment. 

Google Cloud Platform

Presently, every other Google cloud cheat sheet you search will show Google on the third or fourth rank among public cloud vendors. It is one of the tough competitors to Amazon, Microsoft, and IBM. The definition of Google Cloud Platform varies from one source to another. However, the most general notion about GCP is that it is the collection of cloud computing services provided by Google.

The architecture of GCP is based on the infrastructure used by Google internally for end-user products such as YouTube and Google Search. A brief reflection on the history of GCP could establish the foundation for proceeding towards a discussion on GCP’s architecture. 

Know More about GCP:

History of Google Cloud Platform

The origins of GCP serve as one of the crucial elements in every Google cloud cheat sheet. However, mere information about the launch date of GCP is not enough! It is important to note that Amazon and Microsoft ventured into the cloud with IaaS (Infrastructure as a Service) offerings. On the other hand, Google started off with a PaaS (Platform as a Service) offering known as the App Engine.

In April 2008, the first preview of the App Engine was available for developers. In the initial stage, only 10,000 users had the preview of the App Engine. As of May 2008, the number of users reached 75,000, and over 80,000 were on the waiting list. At that point in time, Google let the service open for everyone, and resources were free, albeit limited.

In 2009, Google introduced the option for purchasing resources other than the ones in the free tier. Subsequently, the preview label on App Engine was no more in November 2011. The first bit of criticism came for Google due to the lack of support for popular programming languages such as Java. Google worked on the issue in April 2009 after the initial reviews.

The next important aspect of the history of GCP in this Google Cloud Platform cheat sheet is the second phase. Google came up with the second most popular cloud service known as Cloud Storage in May 2010. This was the first venture of Google into the IaaS market. At that point in time, Google also brought in support for enterprise users with the launch of Google App Engine for Business.

Subsequently, Google introduced Compute Cloud live as a preview in June 2012 as a competitor for Microsoft Azure Virtual Machines and AWS Elastic Compute Cloud. The development of GCP since then has been possible through the introduction of new services. However, it also continues to be a public cloud provider with the least prices alongside additional advantages of big data, container management, and machine learning tools. 

The Architecture of the Google Cloud Platform

Image Source:

The next important element in this Google Cloud cheat sheet is a brief outline regarding the architecture of the Google Cloud Platform. Basically, the Google Cloud Architecture follows multitenancy. It is a variant of computing architecture that involves the creation of one or more logical software instances alongside executing them on top of primary software. The multitenant architecture enables multiple users to work in a software environment concurrently with separate user interfaces, services, and resources. So, how does the architecture of GCP help users?

Benefits of Google Cloud Platform

The benefits of GCP are also a staple inclusion for every Google cloud developer cheat sheet. This list of Google Cloud Platform benefits form the basis of reasons to adopt Google Cloud Platform.

  • Security

Continuing from the above-stated, the multitenant infrastructure of GCP provides exceptional security during service deployment. Security is the prominent concern for the deployment of applications on GCP’s infrastructure. There is no assumption of trust between services, and multiple mechanisms help in establishing and maintaining trust. Other crucial advantages in terms of security are evident in operational and device security, encryption of internet communication, identity and access management, and encryption of stored data. 

  • Better Pricing

The next important advantage of Google Cloud Platform is the facility of better pricing in comparison to the competitors. The facility of billing in minute-level increments alongside discounts for long-running workloads without any up-front commitment. 

  • Faster and Extended Network

The large network of Google is one of the foremost benefits. The investment of Google in the FASTER Cable System in 2016 and in the first private trans-Atlantic subsea cable in 2018 show the extensive plans of Google for expanding and strengthening its network. Furthermore, GCP introduced premium tier and standard tier networks that is the first major public cloud with a tiered cloud network. 

  • Live Migration

The facility of live migration of virtual machines is also one of the common mentions in every Google cloud cheat sheet. Live migration helps in addressing issues such as patches, repairs, software, and hardware updates effectively. 

  • Redundant Data Storage

The plans of Google Cloud Platform for rapid expansion, as well as the facility of redundant backups, are commendable advantages. Google Cloud Storage provides the assurance of 99.99999% durability. Google cloud developer cheat sheet contains information about different types of storage models.

The four types of storage are cold line storage, regional storage, multi-regional storage, and nearline storage. Redundant data storage with automatic checksums helps in ensuring data integrity. In addition, multi-regional storage provides the benefit of geo-redundancy. Therefore, cloud storage stores data in at least two regions so that your data is safe, even in the case of a calamity.

Preparing for Google Cloud interview? Check out these Google Cloud interview questions and get ready to ace the interview.

How to Work on the Google Cloud Platform?

Another crucial addition to every Google cloud cheat sheet is the illustration of basic steps for working on GCP. The best way to learn your baby steps on Google Cloud Platform is through some quick-start guides. The guides are actually activities that involve basic tasks. 

  • First of all, you can learn about the creation of a Linux VM, connect with it and then delete it. This simple task can help in learning about the Google Compute Engine. 
  • The next activity to learn about working on GCP in a Google Cloud developer cheat sheet is storing a file and sharing it. This activity involves the creation of a bucket, uploading a file, sharing the file, and then organizing it into a folder. You can learn about Google Cloud Storage with this activity.            
  • You can gain a basic impression of Kubernetes Engine and Cloud SDK with a simple task of deploying a Docker Container Image. The activity involves using Cloud Shell for configuration of gcloud and running a container image. 

Other basic activities that can be important additions to every Google cloud architect cheat sheet are as follows:

  • Training a TensorFlow model locally in the Cloud with a single worker and in a distributed environment for understanding Machine Learning API.
  • Running label detection on an image by using the Cloud Vision API service.
  • Deploying a small App Engine application by creating a Python application for a basic understanding of Google App Engine.

Products and Services on Google Cloud Platform

The most important content of every Google Cloud architect cheat sheet is the illustration of the products and services. The ever-expanding portfolio of offerings by GCP is also one of its most prominent highlights. The broader classification of Google Cloud Platform’s products and services is evident in the following categories.

  • Computing and hosting
  • Machine learning
  • Storage
  • Big data
  • Networking
  • Databases
  • Computing and Hosting Services

Computing and hosting services by GCP offer different options and find mention in every Google cloud cheat sheet. The options include working in a serverless environment or using a managed application platform. Users could also make the most of container technologies for additional flexibility. In addition, users also have the option of creating their own cloud-based infrastructure for ensuring maximum control. GCP’s Compute Engine is the IaaS offering that gives you a sturdy computing infrastructure. Users could choose the components they want to include. 

  • Machine Learning Services

Machine learning services are also another important entry in a GCP Cheat Sheet 2020. The AI Platform of Google provides different machine learning services; Users can select APIs with pre-trained models having optimization for particular applications. On the other hand, users could also build and train their personal large-scale, comprehensive models by leveraging a managed TensorFlow framework. 

Must Read: Introduction to Machine Learning on Google Cloud Platform

  • Storage Services

Google Cloud’s storage services are also a mandatory element in every Google Cloud cheat sheet. The foremost name that comes to mind with GCP storage services is Google Cloud Storage. It provides consistency, scalability alongside the large capacity for storing data. Persistent disks on Compute Engine could also be primary storage alternatives for instances. Another notable storage service that you can find in almost every Google commands cheat sheet is the Filestore. Filestore provides fully managed NFS file servers. 

  • Big Data Services

Big data services of GCP are also one of the prominent elements in the Google cloud cheat sheet. Big data services include BigQuery for data analysis services, Dataflow for batch and streaming data processing, and Pub/Sub for asynchronous messaging. 

  • Networking Services

Networking services are prominent entries in every Google commands cheat sheet along with other important services. It is one of the commonly used services with App Engine handling networking. The GKE implements Kubernetes Model with networking resources by Compute Engine. The networking services can help in the creation of DNS records, the connection of the existing network to Google’s network, and load-balancing traffic across resources.

  • Database Services

The final mention among the Google Cloud Platform services in a Google Cloud cheat sheet is database services. The assortment of SQL and NoSQL database services in this category of GCP offerings serves as the backbone of GCP’s popularity. Cloud SQL on GCP provides SQL database with the option of MySQL or PostgreSQL databases.

The Cloud Firestore and Cloud Bigtable are two distinct alternatives to NoSQL data storage. Users could also opt for Cloud Spanner that offers a fully managed, relational database service with transactional consistency. Other crucial features of Cloud Spanner include schemas, SQL querying, and automatic, synchronous replication to ensure high availability.

Confused while choosing a cloud hosting for your business? Here are the top advantages of Google Cloud Hosting that will benefit your business.

Products and Services on Google Cloud Platform

The following table outlines a list of the different products and services on Google Cloud Platform in different categories. The table also includes brief definitions of each product and service.


GCP product or service offering

Definition (Functionality)


App Engine

The managed app platform for GCP

Cloud Functions

Serverless functions according to specific events

Cloud Run

Serverless computing for containerized applications

Compute Engine

Virtual machines, disks, GPUs and TPUs

Kubernetes Engine (GKE)

Managed services for Kubernetes or containers


Enterprise-grade multi-cloud or hybrid platform


Cloud Storage

Storage and serving of objects


Archival storage with occasional access


Archival storage with rare access

Persistent Disk

VM-attached disks

Cloud Filestore

Managed service for NFS server


Cloud Bigtable

Low-latency, non-relational, petabyte-capacity database

Cloud Datastore

Document database with horizontal scalability

Cloud Firestore

Highly consistent serverless document database

Cloud Memorystore

Managed Redis

Cloud Spanner

Relational database with horizontal scalability

Cloud SQL

PostgreSQL and Managed MySQL

Data and Analytics


Data analytics or warehouse service

BigQuery BI Engine

In-memory analytics engine

BigQuery ML

Training or serving of BigQuery model

Cloud Composer

Managed service for workflow orchestration

Cloud Data Fusion

Graphical management for data pipelines

Cloud Dataflow

Stream or batch data processing

Cloud Datalab

Managed Jupyter notebook

Cloud Dataprep

Visual data wrangling

Cloud Dataproc

Managed services of Spark and Hadoop

Cloud Pub/Sub

Global real-time messaging

Data Catalog

Management service for Metadata

Data Studio

Collaborative dashboarding and data exploration


Managed platform for genomics

Artificial Intelligence/ Machine Learning

AI Hub

Hosted services for AI component sharing

AI Platform

Managed machine-learning platform

AI Platform Data Labeling

Human data labelling

AI Platform Deep Learning VMs

Pre-configured VMs to support deep learning

AI Platform Notebooks

Managed instances of JupyterLab notebook

AI Platform Predictions

Autoscaled model service

AutoML Natural Language

Custom text models

AutoML Tables

Custom structured data models

AutoML Translation

Custom translation service according to domain

AutoML Video Intelligence

Custom video annotation models

AutoML Vision

Custom image models

Cloud AI Building Blocks

Hosted repository service for AI component

Cloud Natural Language API

Text parsing and analysis

Cloud Speech-to-Text API

Conversion of audio to text

Cloud Talent Solutions API

Job searching with ML

Cloud Text-to-Speech API

Conversion of text to audio

Cloud Translation API

Detecting and translating language

Cloud Video Intelligence API

Video annotation with respect to scene

Cloud Vision API

Recognition and classification of images

Cloud TPU

Hardware acceleration for ML

Diagflow Enterprise Edition

Creation of conversional interfaces

Document Understanding AI

Analysis, classification and search of documents

Recommendations AI

Creation of custom recommendations

Vision Product Search

Visual search for products


Carrier Peering

Peering through a carrier

Direct Peering

Peering with GCP

Dedicated Interconnect

Dedicated private network connection

Partner Interconnect

Connecting on-premise network to VPC

Cloud Armor

WAF and DDoS protection

Cloud CDN

Content delivery network

Cloud DNS

Programmable DNS serving

Cloud Load Balancing

Distribution of loan in multiple regions

Cloud NAT

Translation service for network address

Cloud Router

VPC or on-premises network route exchange


Virtual private network connection

Network Service Tiers

Tiering according to price and performance

Network Telemetry

Network telemetry service

Traffic Director

Management of service mesh traffic

Google Cloud Service Mesh

Network management with focus on service

Virtual Private Cloud

Networking defined by software

Internet of Things (IoT)

Cloud IoT Core

Management of devices and data ingestion

Identity and Security

Access Transparency

Auditing access of cloud provider

Binary Authorization

Security for Kubernetes deploy-time

Cloud Audit Logs

GCP audit trails

Cloud Data Loss Prevention API

Classification and redaction of sensitive data

Cloud HSM

Service of hardware security module

Cloud IAM

Control service for resource access

Cloud Identity

Management of apps, users and devices

Cloud Identity-Aware Proxy

App sign-in feature based on identity

Cloud Key Management Service

Hosted service for key management

Cloud Resource Manager

Management service for cloud project metadata

Cloud Security Scanner

Scanner for app engine security

Cloud Security Command Center 

Service for discovery, search, management and inventory of assets

Context-aware Access

Access control for end-users on the basis of attributes

Event Threat Detection

Scanning for anomalous activity

Managed Service for Microsoft Active Directory

Managed Microsoft Active Directory service

Security Key Enforcement

Two-phase key verification

Shielded VMs

Hardened VMs

Titan Security Key

Two-factor authentication device

VPC Service Controls

VPC constrain data

Management tools

Cloud APIs

APIs meant for cloud services

Cloud Billing

Tools for billing and cost management

Cloud Billing API

Programmatic management of GCP billing

Cloud Console

Web-based management console

Cloud Deployment Manager

Deployment of infrastructure according to template

Cloud Mobile App

GCP manager app for iOS and Android

Cloud Shell

Browser-specific terminal or CLI

Stackdriver Debugger

Debugging in live production

Stackdriver Error Reporting

Reporting app errors

Stackdriver Logging

Centralized logging

Stackdriver Monitoring

Monitoring infrastructure and application

Stackdriver Profiler

CPU and heap profiling

Stackdriver Transparent SLIs

Monitoring GCP services

Stackdriver Trace

Insights into app performance

Developer Tools

Cloud SDK

CLI for Google Cloud Platform

Cloud Build

Continuous integration or continuous delivery platform

Cloud Code

Cloud-native IDE extensions

Cloud Source Repositories

Hosted private git repos

Cloud Scheduler

Managed service for cron job

Cloud Tasks

Execution of asynchronous tasks

Cloud Tools for IntelliJ

Tools for IntelliJ GCP

Cloud Tools for PowerShell

GCP tools for PowerShell

Cloud Tools for Visual Studio

Tools for Visual Studio GCP

Cloud Tools for Eclipse

Tools for Eclipse GCP

Container Registry

Private container registry or storage

Gradle App Engine Plugin

Plugin for Gradle App Engine

Maven App Engine Plugin

Plugin for Maven App Engine

Migration to Google Cloud Platform

Cloud Data Transfer

Tools or CLI for data migration

Google Transfer Appliance

Data transfer box available for rent

Cloud Storage Transfer Service

Inter-cloud transfers

BigQuery Data Transfer Service

Importing analytics data in bulk

Migrate from Amazon Redshift

Migration from Redshift to BigQuery service

Migrate from Teradata

Migration from Teradata to BigQuery service

Migrate from Anthos

Migration of VMs to GKE containers

Migrate from Compute Engine

Migration tools for Compute Engine

VM Migration

Tools for VM migration

API Platforms

API Analytics

API Metrics

API Monetization

Monetization of APIs

Apigee API Platform

Development, security and monitoring of APIs

Apigee Sense

Protection of API from attacks

Apigee Hybrid

Management of hybrid or multi-cloud API environments

Cloud Endpoints

Cloud API gateway

Cloud Healthcare API

Interoperability of healthcare system GCP

Developer Portal

API management portal

GCP Marketplace

Partner and open-source marketplace

GCP Foundational Open Source Projects

Apache Beam

Batch or streaming data processing service


RPC (Remote Procedure Call) framework


Secure container runtime


Connecting and securing services


Serverless framework for Kubernetes


Machine Learning toolkit for Kubernetes


Containerized application management service


Framework for cloud native observability


Machine Learning framework

Google Cloud Platform Certifications

One of the crucial components of this GCP Cheat Sheet 2020 is the list of Google Cloud certifications. The certifications by Google Cloud Platform could help in gaining recognition for different skills in working on GCP. Certifications can help in validating skills in designing, development, management, and administration of application infrastructure and data solutions on GCP. The whole spectrum of Google Cloud Platform certifications involves associate-level and professional level certifications. 

The only associate-level certification of GCP is the Associate Google Cloud Engineer certification. The Cloud Engineer Certification focuses on core Google Cloud Platform technology and is ideal for any beginner in GCP. 

Professional certifications in GCP help in validating role-based assessment and evaluation of design and implementation skills. The list of professional-level GCP certifications is the last element before rounding up this Google cloud cheat sheet presented as follows.

Google Cloud also has another product proficiency certification known as the G Suite certification. It helps in validating the ability of the candidate for collaboration skills using core GCP tools and services.

Final Words

Based on the information in the above-mentioned Google cloud cheat sheet, any reader could get a basic idea about GCP. Google Cloud Platform is a name that resounds prolific expertise and years of experience in data center management. Furthermore, the extensive network of Google and ongoing projects for expansion imply a strong future for Google Cloud Platform.

Therefore, the transition towards a career on Google Cloud Platform can render prolific long-term benefits in terms of professional development. In addition, the appealing remuneration structures for Google Cloud certified professionals also presents clear signs for considering the significance of GCP. If you are interested in a career related to the cloud, why not Google Cloud?    

Getting a Google Cloud Architect certification can help you validate and recognize your expertise on Google Cloud Platform. If you are a Google certified professional such as Google Cloud Professional Data Engineer and thinking to give your skills a recognition, check out our Google Cloud certification training courses. So, start your preparation and get ahead to become a Google Cloud Certified professional.

About Girdharee Saran

Girdharee Saran has a glorious 13 years of experience transforming the way e-learning and SaaS start-ups approach digital marketing for their organisations. He has successfully chartered tangible results, which have proven beneficial. Working in the spaces of content marketing and SEO for a considerable amount of time, he is well conversant in his art. Having taken a deep interest in content and growth marketing, his urge to learn more is perpetual. His current role at Whizlabs as VP Marketing is about but not limited to driving SEO, conversion optimisation, marketing automation, link building and strategising result driven content.

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top