Organizations need time and budget to scale up their own IT infrastructures such as hardware, software, and services. However, with on-premises IT infrastructure, the scaling process can be slow and the organizations are unable to achieve the optimal utilization of the infrastructure. So to scale up the process cloud computing was introduced. Cloud Computing is the paradigm shift that provides computing over the internet. Cloud computing is the use of remote servers on the internet to store, to manage and process data.
Cloud consists of highly optimized virtual data centers that provide hardware, software and services. Organizations can simply connect to the cloud and can use the resources available on it. Some of the most widely used cloud providers are Google Cloud Platform, Microsoft Azure, Amazon Web Services, IBM Cloud, VMWare etc.
In April 2008, Google announced its App Engine to deploy Java, PHP, Node.js applications. Google Cloud Platform (GCP) was made generally available in November 2011 with various services. It uses the same infrastructure as that is used for other services such as YouTube, Google Search.
Services in Google Cloud Platform
GCP has a bunch of services that it can offer and these services are further classified into the domains. There are three main domains of Google Cloud Platform which can host any kind of application:
Compute Engine is the raw server where we can choose the type operating systems we want to work on, configuration, the storage requirement, RAM, CPU everything that is needed for the setup of an environment.
App Engine is the extended version of Compute Engine. This is used when we have to deploy an application or a web server on the Google Cloud Platform. We can simply choose the configuration that is needed for the app or web server.
Cloud Functions are used for back-end processing such as app processing or website processing. It decides the machines on its own that are to be used for the processing.
There are some applications that run inside the containers. If we want to deploy these docker containers on the cloud, we need Kubernetes Engine. We can move that container on the cloud and with the help of Kubernetes engine, we can deploy them. These are deployed on the server as if they are deployed locally.
Whenever we need to store the data that is unstructured or no sequel, we can store it in Datastore Service.
Bigtable and Datastore service are almost the same. The only difference lies between them is that BigTable is used with Hadoop application or any other Big Data application.
Storage service is used for storing various kinds of data and file applications. It is better than Google Drive because it is more secure.
Then we have the SQL service. This service is provided to the databases which are using the structured data.
Spanner Service is very much similar to the SQL service. The difference is that Spanner service is horizontally scalable and SQL service is not. This service is used when we do not have any idea about the usage and the number of users that are going to roll out on the website or applications.
A Virtual Private Cloud (VPC) is created around any kind of instances which are launched in Google cloud. If we want two or more Virtual Machines to interact with each other which are deployed on the Google Cloud we need to put them inside one network so that they can talk to each other.
Regions and Zones
Regions are the areas which consist of multiple zones. Regions are the areas where the servers are deployed. These regions have the backup of the applications in case the data is lost.
There are multiple data servers in a region. We can deploy our application in one server or in multiple servers. We can deploy our applications in other regions. This will help us to keep our applications safe and it will help us to host our applications in different countries.
Need of Google Cloud Platform
With its intense focus on Data analytics and Machine learning, Google cloud comes out on top of all other competitors in the cloud market with regard to data analysis. So if you are interested in data analysis, machine learning or Big Data, the chances of you finding yourself dealing with google cloud services for all these respective fields are quite high.
Google cloud offers live migration. It allows proactive maintenance of all the resources of a given organization without any hindrance in the performance of the virtual machines hosted by that organization. This saves the businesses from the headache of dealing with the lock-ins and downtimes or outages. This was one of the reasons that Evernote migrated to Google Cloud.
Nowadays it can be seen that data is becoming really handy with the help of web applications or web servers. Being a programmer we can deploy apps and scale them in GCP itself. We do not have to worry about the servers’ availability to their respective clients.
Why choose GCP over other services?
GCP lags behind the AWS in case of market sharing. But in recent time it can be seen that Google is leaving no stone unturned and is trying to catch up fast. Here are some of the reasons why we should choose GCP over AWS:
Google has only one type of pricing plan, unlike AWS which is you pay monthly as per your usage. The minimum usage is ten minutes, and it is rounded off to the nearest minute.
Moreover, Amazon offers committed discounts, but only for one virtual machine. Google has also committed to its users. It will pass on any price reductions that the company will achieve due to an enhancement of technology.
Google has been investing in Faster Cable System through which it will be able to provide its Google Cloud and Google App customers speed up to 10 Tbs (Terabits per second).
It considerably increases the performance and affects the cost. It allows for more data processing in less time.
Google offers live migration of virtual machines between the host machines. This helps to keep the business running without any hindrance.
Live maintenance allows the company to repair and update software. It includes security-related programs without rebooting the machines.
BigQuery is an entirely managed data warehouse that allows you to process massive amounts of data at super-fast speeds. It is an innovative tool for cloud warehousing. This helps to do work in minutes which used to take hours earlier.
Google has also revealed tools for machine learning and artificial intelligence.