Basics of Google Cloud Platform (GCP)

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: July 17, 2021

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Google Cloud Platform, as the name implies, is a cloud computing platform that provides infrastructure tools and services for users to build applications and services on top of. Google Cloud Platform is regarded as the third biggest cloud provider in terms of revenue behind AWS in first place and Microsoft Azure in second. Any organization in need of cloud computing should consider Google Cloud Platform for their needs--especially SMBs, which the platform was initially geared toward. Google announced its first cloud tool, Google App Engine, back in 2008, and it continued to add more tools and services until they collectively became known as the Google Cloud Platform later on.

Google's strength lies in big data processing tools, artificial intelligence (AI) and machine learning initiatives, and container support. Google follows its own pricing pattern and routinely boasts that it offers the lowest cost of the top three providers.

Since Google Cloud Platform is a publicly-available product, it's not very difficult to acquire its services. The bigger issue is two-fold: Deciding whether or not the platform is the best option for your business, and planning your migration. Google does offer a free tier for Cloud Platform, as well as a free 12-month trial with credit for organizations that may need to dip their toes in the water.


Any Google Cloud resources that you allocate and use must belong to a project. You can think of a project as the organizing entity for what you're building. A project is made up of the settings, permissions, and other metadata that describe your applications. Resources within a single project can work together easily, for example by communicating through an internal network, subject to the regions-and-zones rules. A project can't access another project's resources unless you use Shared VPC or VPC Network Peering. Each project ID is unique across Google Cloud. A project serves as a namespace. This means every resource within each project must have a unique name, but you can usually reuse resource names if they are in separate projects. Some resource names must be globally unique.

Each Google Cloud project, as a minimum, must have the following:

- A project name, which you provide.
- A project ID, which you can provide or Google Cloud can provide for you.
- A project number, which Google Cloud provides.

Ways to interact with the services

Google Cloud gives you three basic ways to interact with the services and resources.

Google Cloud Console

The Google Cloud Console provides a web-based, graphical user interface that you can use to manage your Google Cloud projects and resources. When you use the Cloud Console, you create a new project, or choose an existing project, and use the resources that you create in the context of that project.

Command-line interface

If you prefer to work at the command line, you can perform most Google Cloud tasks by using the gcloud command-line tool. The gcloud tool lets you manage development workflow and Google Cloud resources in a terminal window.

Client libraries

The Cloud SDK includes client libraries that enable you to easily create and manage resources. You also can use the Google API client libraries to access APIs for products such as Maps, Drive, and YouTube.

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Basics of Google Cloud Platform (GCP)
Basics of Google Cloud Platform (GCP)

Basic Google Cloud services

With a focus on services and applications, GCP gives you the power to build applications and blend assets also known as app modernization. By creating a hybridized model with components, you can stage your workloads in a more efficient way using Google's own experience as a pioneer in technology. The core cloud computing products in Google Cloud Platform include:

Google Compute Engine

Compute Engine (GCE) is the basic service Google offers that competes with the basic, premier service that Amazon offers: hosting virtual machines. An "instance" of virtual machine resources (memory, storage, processor power, network throughput) that is assembled to run like a physical server with the same levels of physical resources.

Google Cloud Storage (GCS)

Often mistaken for Google Drive, GCS is an object storage system, which is to say, its records maintain both the identity and the structure of any class of data given to it. It can hold entire organized databases, raw video streams, or matrices for machine learning models. You can choose among multi-regional, regional, nearline and coldline, each providing specific benefits ranging from maximum availability to the lowest cost storage.

Google Kubernetes Engine (GKE)

GCP's fully managed, hosted staging environment for containerized applications is now generally known as Google Kubernetes Engine (GKE, having originally been launched as Google Container Engine). A container is designed to be executed on any system or server with the underlying infrastructure required to support it. A Linux container still needs Linux, and a Windows container needs Windows, but besides that distinction, a container is extremely portable. GKE recommends an open-source service mesh called Istio.

Google App Engine (GAE)

Google App Engine (GAE) is GCP's service for enabling developers to build applications remotely, using the language of their choice (although Google tends to push Python). GAE supplies the interpreters and just-in-time compilers needed to run high-level programs written in Python, Ruby, Node.js (server-side JavaScript), and other well-known languages.


As Google's first multi-cloud deployment platform, Anthos not only covers hybrid cloud (which incorporates customers' IT assets on-premises) but also AWS-based (with Azure still forthcoming), all managed collectively under the auspices of GCP. Anthos enables an application that incorporates multiple clusters to divide groups of those clusters among cloud platforms. Anthos has been adopted by organizations with highly distributed IT requirements, to run applications as close to the customer as possible.


GCP's tool for applying relational database insights to massive quantities of data is BigQuery. For its query model, BigQuery uses standard ANSI SQL, the language most often used in relational databases. BigQuery uses a columnar, non-relational storage model, which you might think is more difficult to interpret when it comes time to assign relations.

GCP Operations Suite (formerly Stackdriver)

When you need a peek into how your cloud applications are doing, Operations suite has you covered. It monitor, troubleshoot, and improve application performance on your Google Cloud environment. This service collects metrics, events and metadata from GCP, Amazon Web Services (AWS) and other applications. Operations Suite takes in your data and gives you insights using a variety of tools, including dashboards, charts and alerts.

AI and ML

Google is often cited as a world leader in the field of artificial intelligence. AI and ML Platform is a suite of services on Google Cloud specifically targeted at building, deploying, and managing machine learning models in the cloud. AI Platform is designed to make it easy for data scientists and data engineers to streamline ML workflows, and access groundbreaking AI developed by Google. Often used a lot with AutoML (Google's point-and-click ML engine), but in addition, it supports training, prediction, and version management of advanced models built using Tensorflow, and SKLearn.

Google Cloud IAM

Identity and Access Management (IAM) lets administrators authorize who can take action on specific resources, giving you full control and visibility to manage Google Cloud resources centrally. IAM provides a simple and consistent access control interface for all Google Cloud services. Create and manage IAM policies using the Google Cloud Console, the IAM methods, and the gcloud command line tool. IAM is offered at no additional charge for all Google Cloud customers.

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Pros and Cons

GCP has the fastest, simplest, and some of the cheapest primitives for building. Since GCP is built on the Google infrastructure, there are a few facilitations and challenges that follow.


Modern, fast UI: both the web UI and terminal console application for managing Google Cloud projects work very well and are nice to use. There is a good Android App too, although I have not used it.

I like the pricing model for Compute Engine. I think sustained discounts are quite innovative and mean you can get reserved instance pricing without having to pay upfront, or do any work to predict and buy instances. Preemptible instances are also incredibly cheap and are a Yes/No choice when you start a new instance as opposed to the way AWS offers a more complex market.

It feels like GCE is built on top of modern technology e.g. fast boot times, flexible networking, promise of no/low contention for resources.

Google is 100% carbon neutral (some from offset). They are seriously pushing their green credentials and are far ahead of any other provider (note that AWS does have 2 carbon neutral regions).

Live migrate around maintenance events is magic. We've tested it with Google Engineers on live workloads e.g. MongoDB clusters with zero impact.

They are leading the container hosting push. They may be behind many of AWS's features but when it comes to containers, Google lead the technology because they created it and have run it in production for years before productising it.


Their support UI is not up to their reputation. It's difficult to manage users and tickets have arbitrary limits for things like descriptions and attachments, which make it difficult to send debug info. I believe this is because it's based on Salesforce in the background.

Networking pricing is a bit expensive. This is not just Google, but AWS and others too. We get free worldwide private transit with Softlayer and this would be a huge cost on Google for our current load.

The product feels a little experimental. There are regular outages causes by things which should not happen e.g. rollouts to every cloud data centre causing global outages, or software bugs taking services offline. This makes it hard to rely on, even if you deploy across multiple zones or regions.

They are quite a long way behind AWS and Azure in terms of product range. This means you get a better ecosystem elsewhere. Google has been releasing at a fast pace but there's a way to go.


Google is one of the largest cloud customers in the world due to its search engine and mass-scale consumer apps, and therefore, is often first to create cloud services and architectures internally that later lead to widespread adoption, such as Kubernetes. Machine learning is another piece where Google was one of the first to require ML inference for mass-scale models.

81% of organizations are working with two or more public cloud providers. A multi-cloud strategy gives companies the freedom to use the best possible cloud for each workload. Anthos builds on the firm foundations of GKE, so you can build out hybrid and multi-cloud deployments with better cloud software production, release, and management-the way you want, not how a vendor dictates. That is key to how a healthy cloud ecosystem works. an enterprise can seamlessly connect directly to their data across Google Cloud, Amazon Web Services (AWS), and (soon) Microsoft Azure, managing large-scale data analysis fast, without having to move or copy data sets, on a single user interface.

For companies that don't want to spend a lot of time learning and dealing with the complexities of AWS, I recommend looking at Google Cloud. If I had to start all over again, I would still happily choose Google Cloud.

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Basics of Google Cloud Platform (GCP)

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