Data Science with Cloud Computing

Data Science with Cloud Computing

Data Science with Cloud Computing

Data Science is one of the most popular fields in today’s world. With the increase in loads of data generated, their storage is a big issue which all the enterprises have to deal with. Today every sector is dependent on the programs and applications for faster business growth. Their operation requires a certain amount of data and it variably increases with day to day tasks. Here, cloud computing comes into play.

What is Cloud Computing?

The emergence of cloud computing has undergone several phases before making a go-go in its present form. In 1963, Defense Advanced Research Projects Agency, offered MIT with $2 million project which includes a requirement MIT develop technology allowing for a “computer to be used by two or more people, simultaneously. This was the precursor to what is now called as Cloud Computing. Then In 1969, J.C.R. Licklider developed ARPANET (Advanced Research Projects Agency Network), he name this primitive version of the Internet  “Intergalactic Computer Network,” in which everyone is interconnected by way of computers, and able to access information from anywhere.

Thereafter, the real game to develop their own cloud based mechanism started among the enterprises and Amazon was the first to develop “Amazon Web Services” in the early 2000’s. Later in 2010, Microsoft launched “Azure” to manage applications and services through a global network of data centers.

Cloud is used as a “metaphor” for Internet which doesn’t require any physical storage devices. This “cloud” is of 3 types; Public, Private and hybrid. Public clouds are the one which you can use for free. For example, Google Docs and Gmail where you can upload, download and access the files wherever you want. The private are more secure and are premium i.e. you have to buy it. Big companies and firms use these feature services for e.g. banks. Hybrid is combination of both public and private.

Cloud Computing in Data Sciences

Data Science is the combination of computer sciences tools and statistical methods for processing of data. Simply put, it is the knowledge discovery to gain insights about the data. And when “data” is being talked about, A BIG data comes into picture. How such big data can be handled? The solution is Cloud Computing. After 2010 an abundance of data starts spurting in the industry. Cloud computing is shaping this ecosystem by managing the flood of data. The popular charts on i-tunes which you listens to, changes every day, so does your choice. Nothing is static, right? This dynamic behavior is tackled by importing your favorites to the ‘cloud’ which works as your repository. No need to worry about the storage of your phone, tablet or desktop.

This rise of big data is becoming too much to handle which is sparking the cloud revolution. Almost everything like- collection, analysis, modeling etc in the data processing stages, artificial intelligence is dominating. To transform this intelligence to business intelligence, technology has to transform into up-to-date from out-of-date. Reaping maximum benefits from this intelligence requires a strategy. Cloud networks are the guiding forces which will take data sciences to the right direction. The data management through cloud will reap maximum benefits and companies are relying on cloud computing by investing in the cloud resources.

Cloud Architecture and Big-Data

Cloud computing architecture defines the basic components and functionality which makes up the whole system. The front end and the back end are the two components. The front end refers to the interface and the client’s network which is required to access the cloud computing system. This is the client part of cloud computing system.

The back end of the system refers to the cloud which consists of various computers, servers and data storage systems of computing services. It involves all the resources required to provide cloud computing. It comprises of application services, enormous data storage, virtual machines, security mechanism, servers, infrastructure etc.

The cloud servers contain every data whether big or miniscule. These servers store data, controls the system traffic, monitors safety and security mechanisms and client demands to make sure smooth functioning of the system. Data lost could cause a huge loss of time, money and effort. Cloud saves this by updating data repeatedly and granting access to services without having to worry about physical storage.

Service Models

Cloud computing is regularly expanding to fulfill the business needs of an IT enterprise. To fulfill this need, cloud services are categorically classified into 3 most common service models. These 3 services are ‘Saas’, ‘PaaS’, and ‘IaaS’.

Saas: 

  • Software as a Service.
  • referred to as ‘on-demand software’, end-user has their access to services through browser but no administrative control.
  • Most widely used service model. Applications are centrally hosted on the cloud server and accessed by customers over internet.

Iaas:                         –

  • User has greater control on software, application and the OS.
  • Providers responsibility is deployment, management, and security.

Paas:    

  • Cloud user can develop and deploy its own application. Cloud provider limits its responsibility to look after the infrastructure platform.
  • Easier for a developer as s/he can concentrate on development without having to worry about infrastructure.

Conclusion

We see that how much data management is necessary. The challenges can be overcome by cloud computing as it acts on the most recent data. It is flexible, cheaper and faster. Cloud computing has revolutionized the IT industry thus providing better services and optimizing business change.

Leave a Reply

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

Looking for Online Training