Analyzing and storing Data are two important challenges for every organization, whether that is small or large. Cloud Computing for Data Science is a solution that companies prioritize in order to store such data economically and securely.
This cloud computing system has given a rise to the trends of hiring skilled data analytics, data engineering and above all data scientists.
Apart from all the skills which a data scientist generally processes like analysis, statistics and programming, a data scientist is also expected to work on new platforms in which the organization stores or collects all the data.
A key skill that a data scientist should have is to write production-quality codes models and to deploy them into cloud environments. Every data professionals should be generalists who can adapt and skill their projects.
Advantages of cloud computing and data science:
Data science and Cloud Computing systems go hand in hand. The work of a data scientist is basically is to analyze the different types of data that are stored in the cloud.
Since the volume of data is increasing, big firms store data online, and data scientists are increasingly in demand.
Take a look at the type of data and data scientist is likely to work in the cloud are:-
- Going through the structured, Semi-structured and unstructured data.
- Analyzing the data to draw insight.
- Going through the various sets of data, irrespective of the size and format, etc.
By going through all the aspects the only problem with such data is it often disparate silos, given that the storage is much cheaper, the open tool and platform is now available for all the data scientists.
Data as a Service (DAAS) :
DAAS is a very popular and renowned service with a concept of cloud-based data services.
This is generally provided by all the data vendors which are used in cloud computing services to store all kinds of data processing, data integration, and data analytics services to all the companies who are using the network services or network connections.
This is the reason why data as a service is used by all the companies to target all kinds of audiences by using the data. This helps automate all the services and the productions, creating better products according to the market demand.
And all these things always help a business to increase its profitability which in turn gives an edge over its competitor.
Data as service is quite similar to software as a service, or infrastructure as a service; these are very common terms that are usually used in the tech-world. And DAAS is comparatively new in Cloud Computing in Data Science and also gaining its popularity with the passing phase time.
It is because of the fact that DAAS Cloud Computing service, which is provided by the companies was not equipped initially to handle the massive loads of data that were the part of the DAAS.
These services can only manage a small amount of data storage rather than processing of data and analyzing the data on a large scale. Previously, it was difficult as well to manage large data volume over a network as the bandwidth used to be not so strong and was limited.
So, these all things have changed with the passing phase of time and low-cost cloud storage and an increase in bandwidth have made data as a service to the very next step.
Famous Cloud computing systems for data science:
- Amazon web service.
- Microsoft Azure.
- Google Cloud.
Amazon web service :
Amazon web service is nothing but a cloud computing system that is basically a subsidiary of Amazon. Amazon web service is the most popular cloud computing platform for data science, it was launched back in 2006.
AWS provides various products for data analytics which also includes “amazon quick sights” which is basically a business analytics service along with that “Amazon RedShift”, “Amazon Kinesis” is used to calculate real-time data analytics, “Amazon EMR” etc.
Amazon web service also provides products for the databases which includes “Amazon Aurora” along with “Amazon DynamoDB” All these products are generally used by big companies like Netflix, Amazon prime video, NASA etc.
Microsoft Azure is also a cloud computing system that has been designed by Microsoft back in the year 2010. It is a very popular cloud computing system in the case of Data analytics and data science.
Name of some Microsoft Azure data analytics and data science are “Azure Stream analytics” which is a streaming analytics tool. Azure Databricks which is a Apache spark analytic tool, Data factory which is a Hybrid Data Integration etc.
Google cloud computing system is a platform which is provided by Google, it provides the same infrastructure that google uses in their internal products like youtube, Google search, Google mail etc.
Google clouds provides various products such as Data flow which is a Streaming analytics, Data Proc which is a running apache hadoop tool, Google data studio which is a Visualization Dashboard etc.
These are the cloud computing Data science tools that are currently trending worldwide, as because these are powerful and very efficient for all the cloud computing data scientists.
Some of the Study materials of Cloud computing Data Science are :
Future of Data science and cloud computing –
To maximize profits, businesses use two items today: investing in big data, and keeping it in the cloud.
Cloud-based processing of data leads to significant cost savings and faster decision making. With a huge demand in both the fields and billions of dollars of investment both are here to stay.
This is what Cloud computing for Data science is all about, after the launch of cloud computing system the work of a data scientist has become very easy and has also become more effective, because everything has become machine learning that is why humans don’t have to use their mental strength anymore. Now it has also become easier to keep the vast data storage at one place too.
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