What is Google Cloud Platform and Why You Shouls Use it?

Summary

• The Google Cloud Platform is essentially a public cloud-based machine whose services are delivered to customers on an on-the-go basis through service components.
• The public cloud allows you to use its resources to empower the applications you build, while also reaching a wider customer base.
• While Google does offer and compete with virtual machine hosting services similar to Amazon’s web services, its main service model revolves around developing and deploying more modern, containerized applications.
• GCP’s competitive strategy on price is to provide discounts for continuous use, customized use, and committed use.
• Currently, GCP’s target core users appear to be enterprises ——– small to medium or large enterprises that have entered the modern application model——- they need a more cost-effective and efficient deployment.

The Role and Principles of Google Cloud Platform

Google Cloud Platform is a computing resource provider that deploys and runs applications on the web. Its specialty is to provide individuals and businesses with a place to build and run software that uses the network to connect to users of the software. Think about it, thousands of websites run on a network of “very large” (very large, but very distributable) data centers, and you’ll understand the basic meaning.

When you run a website, an application, or a service on the Google Cloud Platform (GCP), Google records all the resources it uses — specifically, how much processing power, data storage, database queries, and network connectivity it consumes. Instead of renting a server or DNS address on a monthly basis (which is what you do with a regular website provider), pay per second (competitors charge by minute) and you can get a discount when your customers use your service heavily on the network.

Significant features of Google’s cloud platform

So what are you doing on the cloud platform, and why are you doing it on Google’s cloud platform? You use the cloud platform when you want to present the services you present to users, customers, or your colleagues as an app, not a website. Maybe you want to help the housebuilder estimate the size and structure of the cupboards they need to rebuild their kitchen. Maybe you’re analyzing the performance statistics of athletes who are testing for college sports clubs, and you need complex analytical methods to tell the head coach who can improve. Or you may be scanning hundreds of thousands of pages of archived newspapers, and you need to build a scanable index that dates back decades.

When you want to build and run an application that somehow harnesses the power of a hyperscale data center, you’ll use a cloud platform like GCP: either to reach global users, either to borrow complex analytics and AI capabilities, or to leverage massive data storage, or to take advantage of cost-effectiveness. You don’t pay for the machine, you pay for the resources the machine uses.

Google’s cloud platform is seen as having a competitive advantage.

• Automate the automation of modern applications. An app is made up of many moving parts, which is why some developers like to build their own apps in the cloud (“cloud native”). Google is the ancestor of Kubernetes, an app coordinator made up of many components. In the early days, Google took a proactive approach to deploying these multi-faceted application automation to the cloud: for example, to open itself to Kubernetes, an automated platform that was originally designed to help developers using Cloud Foundry deploy apps from the dev platform to the cloud.
• Creative cost control. As you’ll see later, Google’s strategy with GCP is not so much a low-cost leader as a cost-competitive ness in some “sweet spot” scenarios. For example, Google provides a lifecycle manager for its data storage, which can uninstall or delete objects that have not been used for more than 30 days.
• More friendly to first-time users. A cloud service platform can be an indigestible concept for a novice. Just as many consumers are not obvious about the use of microcomputers, the public cloud is a strange behemoth for those who are used to seeing and touching the machine. GCP provides examples of the steps of many of the most common tasks — for example, rotating a Linux-based virtual machine, as if you were claiming and setting up your own, brand new PC out of thin air.

Services for Google’s cloud platform

Cloud services are hard to understand. So to help you get a clearer understanding of Google’s cloud platform, here are the main services that GCP operates.

• Google Compute Engine (GCE) is directly competing for the service that puts Amazon Web Services on top: hosted virtual machines (VMs), servers that are fully software-ready.
• Google Kubernetes Engine (GKE), formerly known as Google Container Engine, is a more modern platform for containerized applications (stored in containers often referred to as “Docker containers”) designed for deployment on cloud platforms.
• Google App Engine provides software developers with tools and languages such as Python, PHP and, now, Microsoft. NET language that builds and deploys web applications directly on Google’s cloud platform. This is different from building applications on-premises and then deploying them remotely in the cloud;
• Google Cloud Storage is gCP’s data store, which means that it accepts any amount of data and presents it to users in any of the most useful ways– — such as as files, databases, data streams, out-of-order data lists, or multimedia.
• Nearline is a way to back up and archive data using Google cloud storage — data you don’t necessarily think of as a database– that can be accessed by one user, usually no more than once a month. Google calls this model “cold storage” and has adjusted its pricing model to take into account this low utilization to make Nearline more attractive for purposes such as system backups.
• Anthos, announced last April, is a GCP system for organizing and maintaining applications that may be Google-centric but may utilize AWS or Azure (“Cloudy Services”) resources. Consider that the code base of an application is hosted by Google, but it borrows an AI feature from AWS and stores its logs in object storage on Azure.
• BigQuery is a data warehouse system that uses Google cloud storage and is designed for a very large amount of highly distributed data to execute SQL queries in multiple databases at different levels of structure. Unlike traditional, row-based, record-oriented SQL relational database indexes, BigQuery leverages a column-based storage system where recorded components are stacked on top of each other and flow to parallel storage systems. This method of organization is useful in analytical applications, which collects a wide range of simple, often general, data elements for the relationshipbetween.
• Cloud Bigtable (formerly BigTable) is a highly distributed data system that organizes relevant data into a multi-dimensional collection of key/value pairs, based on a large-scale storage system created by Google for itself to store search indexes. Such a collection is easier to manage for analytics applications than for large indexes of large relational databases, because records of multiple tables must be connected at query time.
• Cloud SQL (not yet ready for public use) hosts more traditional, relational database tables and indexes, using a single GCE instance that can scale on its own to meet the performance needs of the database.
• Cloud translation, text-to-speech, and speech-to-text, as the name suggests, are used to utilize Google’s existing oral and written language management capabilities for customized applications.
• Apigee is a modeling system for making and managing APIs, which uses the Web as a communication medium to make service calls to server-based functionality. Apigee users can model, test, and deploy mechanisms for their existing Web applications so that they can use APIs to discover and monitor how Web users use these API calls for their own purposes.
• Istio is an interesting “phone book” for modern, scalable applications that are distributed as separate components in a separate component called microservices. A traditional, continuous application knows where all its features are, while a microservices-based application needs to notify users through a service mesh. Istio was originally developed by an open source partnership between Google, IBM and ride-sharing service Lyft.
• Cloud Pub/Sub is a mechanism that replaces the message queueused by middleware in the early client/server application era. For applications that are designed to work together without explicit connections (“asynchronous”), Pub/Sub acts as an event post office, so an application can notify other applications of progress or requests they may have.
• Cloud AutoML is a set of services designed to enable applications to take advantage of machine learning — detecting perceptible patterns in large amounts of data and taking those patterns in programs.
• Cloud Run is the latest announced service that enables software developers to deploy their applications to Google’s cloud, building and running programs on-premises rather than in the cloud, and hosting them locally rather than in the cloud.

This is far from a complete list of Google Cloud Platform services, but it does introduce you to the main items. In fact, many of the company’s services (all of which can be found in the list on this page) are applications or reconfigurations of other services — a way to use services that perform a wide range of functions for more specific purposes.

So what is the role of G Suite and google docs in all this?

Yes, G Suite is a software-as-a-service (SaaS) that is delivered to you through Google’s cloud platform. But like Gmail, it’s not part of Google’s cloud platform. We often use the word “service” to refer to what Google is exchanging here, but for the time being, gCP is seen as a product. A productive application is a different department than the managed application and interface functionality.

What’s more, when you become a GCP customer, the agreement you have is completely different from the G Suite agreement. GCP addresses the expectation that you will use its cloud-based resources to create your own services and applications that are likely to be used by others other than yourself. As a result, Google has a commitment to service levels, and it has a very clear description of each service.

What is the cost of using the Google Cloud platform?

Each service consumes the basic resources of cloud computing: processor power, memory, data storage, and connectivity. Like other cloud service providers, Google charges GCP customers based on the resources consumed by these services. So whatever you choose to do with GCP, you pay for the resources they consume. (As you can imagine, BigQuery and BigTable incur some significant costs in terms of data storage consumption).

The formula for determining the actual price of resource consumption is actually somewhat complex. Google does offer a pricing calculator that uses the latest updates. But to use this calculator, your rough estimate of the resources you plan to consume needs to be in a surprising range. For example, to get a price estimate for Google Kubernetes Engine, you need to know the maximum number of compute nodes you want to scale to, how much persistent disk storage your application needs (as opposed to instantaneous storage), and which availability area you feel is most effective for load balancing, and so on.

Amazon AWS sets the standard through its pricing model for virtual machine instances. Virtual reality machine instances have a “build”, just like a real server. It has a fixed amount of RAM, a fixed number of virtual CPUs, and a base layer of file storage. Google Compute Engine, like its competitors, has its own VM instance selection. It calls these instances pre-defined, and its base price (as of this writing) is just over $0.03 per virtual CPU per hour and $0.004 per gigabyte of storage per hour. However, Google will then recalculate these numbers on a second basis, with a minimum interval of 60 seconds.

The GCP then discounts certain usage patterns for GCE and other services. Google claims that these discounts reduce the average cost of its cloud services compared to its Amazon and Azure peers.

To sum up, Google’s expertise is not a classic virtual machine. While you can imagine that deploying virtual machines on Google’s cloud platform is more cost-effective than AWS, especially on custom instances, GCP is increasingly positioning itself as the host for containerized applications — a modern deployment and management system that provides a modern deployment and management system for applications designed in the cloud age

How did Google’s cloud platform perform compared to its competitors?

Amazon and Microsoft operate their own cloud platforms, called AWS and Azure. GCP is their competitor, and while GCP ranks third in terms of market share and revenue, it is a robust competitor, with its unique features and services giving it an edge in some scenarios.

If the big three public cloud providers are really likened to department stores, and Amazon AWS is… Well, Amazon, with its huge service choices on the shelves, there’s no easy way to tell, then you can say that Azure is like Target: it likes to position itself as a smarter service choice, based on an internal understanding of these needs, to meet demand.

In this parable, Google’s cloud platform is like Ikea. It starts with selling itself to you based on the overall experience. It tries to make you feel comfortable and at ease. It offers a unique and unexpectedly diverse collection of features and quirky, low-cost and premium, side-by-side, perfect blends together. And it openly admits that it’s not the only game.

Google Cloud Platform VS Microsoft Azure

Azure’s original service (then “Windows Azure”) was intended as a cloud-based deployment platform for any of Microsoft’s. An application written in the NET language. As a result, Azure has built its own portfolio of services organically based on close collaboration with software developers. Therefore, an accurate description of Azure’s core customers can be summed up as a “Visual Studio user.”

The GCP began with a consumer business model: distributed software scheduling, one of the core functions it originally created for itself, distributed software scheduling. It doesn’t help you deploy software as much as it helps you or your organization build software. As the creator of Kubernetes, Google’s success has been to make software distributed globally. It addresses the issue of distributing updates to its search engine and email services, and then narrows the solution to a form that a small business can use.

Any enterprise that knows what distributed software is, let alone what it wants to do with these things, is already quite technical. But that’s not the market Google wants to cater to. So it’s trying to make the technology more accessible, in part, no different from instructing home gardeners on how to make better use of nuclear reactors.

This eventually became a key difference between Azure and GCP: for those who may not know much, Google has made even more progress (so far) in adapting its services to those who may not know much. You may be able to master BigQuery or cloud storage more easily.

Google Cloud Platform VS. AMAZON AWS

In recent times, Google has avoided taking a confrontational stance against Microsoft. In fact, the two companies have worked together more than in previous years, compared with Microsoft’s. The appearance of the NET language platform in Google App Engine shows this.

In fact, Google has trained Amazon, the cloud service leader, almost all of its marketing efforts. To do this, here’s how it positions itself.

• GCP is not intended to upend AWS’ position as a leading host for virtual machine instances. As a result, it provides alternatives, most notably custom instances, as well as pricing models that can benefit some customers. Virtual machines may be old deployment models for software, but no cloud service provider can give up a foothold in this service and expect to continue to be considered a player.
• Amazon stuck to the last minute to produce its own Kubernetes engine. After a while, it’s clear that i don’t want to promote a deployment model that cuts into your main line business. As a result, Google, as the forerunner of Kubernetes, remains the leader of Kubernetes in the public eye, and has been taking a winning lap. Another argument in Google’s favor has yet to be refuted: Amazon’s Kubernetes system is Amazon-centric, while GCP (now Anthos) is designed to answer corporate customer demands and avoid being locked in by vendors.
• Recent lying reports from multiple sources have shared the view of cloud computing customers that Amazon’s rich range of service options, in terms of its sheer size, would be detrimental to them. None of the three sources agreed on where AWS customers should start. Google can take advantage of this by focusing on successful services that actually require its customers, rather than focusing too much on experiments and beta tests that don’t sink the company if they fail.

In any healthy economy, most major markets hate a three-way monopoly. Normally, the safest bet analysts can make is that the third-ranked player will be shaken out and must be content to provide an “alternative” product or service for the niche market.

But Google has a luxury that No. 3 players in any market don’t have: the role of No. 1 in a different, almost single-handed market: online advertising. Its cloud services can be allowed to mature and find their own audience, just as the company’s survival does not depend on them. A former Microsoft CEO has warned Google that his company has been dogged, tenacious, tenacious, tenacious and tenacious. But now he’s gone. And Google’s cloud platform has every reason — including all the time it takes — to keep going.