Cloud Computing and GCP Introduction

Cloud Computing

What is it?  

Ah!., If you feel like this, just wait a minute and take a deep breath. I know this would be the most boring question we've ever seen. Let me try to show my view and understanding here :-)

In simple terms, cloud computing is the practice of delivering the services in the various aspects in servers, storage, networks, databases and applications and many. In this model, the consumers only pay for the usage of resources and avoid creating upfront investment(This includes implementation cost, data center deployment, maintenance cost, and others). 

This eliminates the purchasing cost and procuring expensive hardware/software numbers. And, this model is called the “pay-as-you-go” model where the companies focus on rapid innovation and development and not the infrastructure things. 

I hope this description clears out what is cloud computing. Still, you can explore more about this by visiting the GCP link. 

Let us move on to the types and available solutions,

In IaaS, we rent the IT infrastructure, which contains VM, networks, storage, database, containers and so on. In PaaS, we are eligible to have on-demand services like deployment components, Kubernetes. This would be very convenient for developers to only focus on the development, not the underlying infrastructure. 

SaaS is a way of delivering software applications over the internet on a subscription model. 

A good example of SaaS is your Gmail email account. You are subscribed to your email by signing up for it and use the email software that is written, maintained, secured, and managed by Google.

Alright,

why does GCP provide a cloud solution?

GCP's initial release was on October 6, 2011. Since then it has become one of the most used public cloud platforms and is continuing to grow. GCP offers a suite of cloud services that run on the same infrastructure that Google uses to host their end-user products such as Google search, Gmail, and YouTube. This makes it important because Google not only continues to innovate for its customers but also benefits from its own investment into the platform. Google began operations by launching its Google App engine back in 2008. Since then we have seen multiple other services introduced and the list keeps on growing and rocking. 

How does the GCP spread across the regions?

GCP services are located across North and South America, Europe, Asia, and Australia. These locations are further divided into regions and zones. A region is an independent geographic area that consists of one or more zones. 

When you read this post, here is the count. Please do check this link for the latest update. 

When you deploy a cloud resource, they get deployed in a specific region and in a specific zone within that region. Any resource deployed in a single zone will not be redundant if the zone fails, the resource will fail too. If you need fault tolerance and high availability, you must deploy the resource in multiple zones within that region to protect against unexpected failures. 

A disaster recovery plan will be needed to protect your entire application against a regional failure. All regions are expected to have a minimum of three zones:

Compute services

	Compute Engine: helps to create excellent VMs in GDC.
	App engine: allows you to deploy the application on the fully managed platform supported by Google.
	Kubernetes Engine: provides the ability to create containers on GCP.
	Cloud functions: allow us to run the code without any configuration runtime. Yes indeed, the serverless model.

Storage and Database services

	Cloud storage: object storage service used to store any format of data objects.
	Cloud SQL: fully managed database service which helps to create MySQL and PostgreSQL databases in the cloud.
	Cloud BigTable: NoSQL database for Non-RDBMS requirements. This can scale up to petabytes storage easily and is useful for data analysis frameworks and can be integrated with big data tools like Hadoop.
	CloudSpanner: relational database system helps to provide scalable and consistent storage solutions.
	CloudDataStore: Suitable for storing the key-value pair of data. Though it can be related to BigTable, this still posses sharding and replication functionalities uniquely

Network services

	Virtual Private Compute helps to create an isolated network environment for GCP resources.
	Cloud Load balancing: allows to distribute and balance the incoming traffic to multiple compute engines, and also performs the auto-scaling mechanism based on the configuration.
	Cloud Interconnect: using this service, leverage the possibility of connecting the on-premises data center to GDC.
	Cloud Content Delivery Network: useful for caching the contents to desired edge points.
	Cloud DNS: high available DNS service to manage the records and zones.

Bigdata

	Bigquery: DW(DataWarehouse) database dedicated for massive datasets and provides faster performance to query results
	Cloud DataFlow: allows real-time, batch and streaming data processing tasks and also helps to set up the pipeline.
	CloudDataProc: Dedicated to performing/run on apache Hadoop and spark clusters.
	CloudDataPrep helps to clean the unstructured as well as structured data sets.
	Cloud pub-sub: allows us to publish and subscribe to streaming data for big-data analytics.
	CloudDataLab: explore and visualize the result data sets.

Hope this would be a better intro of Cloud basics,

and you enjoy the post like this :-).