Why Cloud Computing is Important in Data Science?

Consider a small company that wants to use data analytics to improve its services and gain an edge over its competitors. In addition to generating data, this company uses third-party data to gain insights. But how can this data be used effectively? After all, this is not Google or Facebook! It does not have the resources. Or the financial capability to store large amounts of data on local servers for data analysis. This is where cloud computing comes into play! Even before this company can use Data Science, it must first focus on Cloud Computing.

Are you wondering what role cloud computing plays in this? 

What role does it play in Data Science? 

We’ll get to that in this article, but first, let’s talk about cloud computing.

What is Cloud Computing?

Cloud Computing lets organizations leverage different computing services such as databases, servers, software, artificial intelligence, data analytics, and many more on the Internet, which is known as the cloud. These organizations can run their applications on the best data centers in the world at minimum cost. 

In addition, this ensures that small companies or those in emerging economies can use this technology for ambition. And complex projects that would otherwise be quite expensive. The same applies to Data Science. With cloud computing, Data Analytics and Data Management have become much simpler for Data Scientists. Let’s find out how!

Why is Cloud Computing Important in Data Science?

Let us think for a second that there was no Cloud Computing for Data Science. Then companies would have to store the data in servers and every time a Data Scientist needed to do data analysis or get some data from teh database, they will have to transfer the data to their system from the servers and then perform the analysis. 

Can you think of the problems that come with it? 

This is not just some amount of data we are talking about, this is a huge amount of data that data analysis needs. 

Furthermore, creating servers for data is highly expensive, and while large corporations can easily manage this, it is completely different for smaller businesses. These smaller businesses are unable to use servers since they require space to store them. These servers necessitate ongoing maintenance and repair, as well as backups in the event that something goes wrong. Having servers also necessitates extensive planning, and firms may end up with more or fewer servers than they want to be based on their data requirements. This is where cloud computing comes into play. 

Companies can use the cloud to house their data and no longer have to worry about servers because the cloud provider now handles it! Companies can customize server architecture in the cloud to meet their specific demands, and they can even save money by only paying for the data they use in the cloud.

Cloud computing has democratized data in a way that is unprecedented in today’s world. Smaller businesses can now undertake data analytics and compete in the market with larger multinationals without incurring the exorbitant fees associated with Data Science. Data Science combined with Cloud Computing has become so popular that it has spawned Data as a Service (DaaS).

What is Data as a Service?

With the emergence of cloud-based data services, Data as a Service (DaaS) is becoming a popular notion. Data as a Service (DaaS) is a service given by data vendors who employ cloud computing to deliver data storage, processing, integration, and analytics to businesses via a network connection. As a result, firms can utilize Data as a Service to better understand their target audience through data, automate some of their manufacturing, design better products based on market demand, and so on. All of these factors boost a company’s profitability, giving it a competitive advantage over its competitors.

Data as a Service is comparable to Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service, which are all common and everyone has heard of it. DaaS, on the other hand, is a relatively new concept that is just now gaining traction as a result of the growing demand for Cloud Computing in Data Science. However, DaaS is gaining popularity as a result of the fact that traditional cloud computing services were not designed to handle the large data loads that are an essential feature of DaaS at first. Rather than data processing and analytics on such a vast scale, these services could only manage basic data storage. Managing enormous data quantities through the network was also problematic in the past due to bandwidth limitations. However, things have evolved over time, and now Data as a Service is the next big thing, thanks to low-cost cloud storage and greater bandwidth!

In fact, by 2023, it is expected that over 90% of large firms will be using DaaS to create revenue from data. Data as a Service will also enable diverse departments inside large corporations to seamlessly share data and receive meaningful insights, even if they lack the necessary data infrastructure in-house. As a result, DaaS will make data exchange for businesses much easier and faster in real-time, increasing a company’s profitability.

What are some Cloud Computing platforms for Data Science?

  • Amazon Web Services : Amazon Web Services is an Amazon company that provides cloud computing services. It was founded in 2006 and is now one of the most popular data science cloud computing platforms. Amazon QuickSight (business analytics service), Amazon RedShift (data warehousing), AWS Data Pipeline, AWS Data Exchange, Amazon Kinesis (real-time data analysis), Amazon EMR (big data processing), and more AWS data analytics products are available. Amazon Web Services also has database products, such as Amazon Aurora (a relational database) and Amazon DynamoDB (NoSQL database). Netflix, NASA, and other well-known firms are among those who use AWS.

  • Google Cloud: The Google Cloud Platform is a Google-provided cloud computing platform. It offers businesses the same infrastructure that Google employs for its own internal products, such as Google Search, YouTube, and Gmail. BigQuery (Data warehouse), Dataflow (Streaming analytics), Dataproc (Running Apache Hadoop, Apache Spark clusters), Looker (Business Intelligence Analytics), Google Data Studio (Visualization Dashboards, Data Reporting), Dataprep (Data Preparation), and other Google Cloud products are available for data analytics. Twitter, PayPal, Vodafone, and other well-known organisations are among those who use AWS.

  • Microsoft Azure: Microsoft Azure is the company’s cloud computing platform. It is a prominent cloud computing platform for data science and data analytics that was first published in 2010. Azure Synapse Analytics (Data Analytics), Azure Stream Analytics (Streaming analytics), Azure Databricks (Apache Spark analytics), Azure Data Lake Storage (Data Lake), Data Factory (Hybrid data integration), and others are some of the Microsoft Azure data analytics offerings. Databases such as Azure Cosmos DB (NoSQL database), Azure SQL Database (SQL database), and others are supported by Microsoft Azure.
Why Cloud Computing is Important in Data Science?

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