Why is Cloud Computing Important in Data Science?



Consider a small business that wants to employ data analytics to enhance its services and beat off rivals. As well as accessing data from outside sources, this business also generates some data. How, though, can this data be used is the question. Since Google and Facebook are not these little businesses, In order to store a lot of data on local servers for data processing, it lacks the financial and resource capacity. Cloud computing saves the day in this way! This business must prioritize cloud computing before it can apply data science.

Do you, however, wonder how cloud computing fits into all of this? What role does it play in data science? We’ll discuss that in this essay, but let’s first define cloud computing.


What is Cloud Computing?


With cloud computing, businesses can access various computer services, including databases, servers, software, artificial intelligence, data analytics, etc., over the internet, or “cloud,” as it is known in this context. These businesses can run their apps for very little money in the top data centers in the world. As a result, significant and sophisticated projects that would otherwise be quite expensive can now be undertaken by small businesses or those in developing economies. In the field of data science, this is also accurate. Thanks to cloud computing, data scientists now find it much easier to manage and analyze their data. We’ll see how.


Why is Cloud Computing Important in Data Science?


Take a moment to consider a world without cloud computing for data science. Then, businesses would have to keep data locally on servers. Whenever a data scientist needed to analyze the data or extract information from it, they would have to move it from the central servers to their system before starting their research. Do you have any idea how complicated this can be? This is a substantial amount of data, as businesses need enormous amounts of data for their data analysis.


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In addition, setting up servers for data is very expensive, and while big businesses can manage this quickly, it is very different for smaller firms. Due to the need for storage space, servers are not an option for these smaller businesses. Backups are also necessary for these servers in case something goes wrong, and they need to be constantly maintained and up-to-date. A company may purchase more or fewer servers than necessary depending on their data requirements, which also requires extensive planning. Thus, cloud computing enters the picture. Companies can host their data in the cloud, relieving them of their server-related worries since the cloud provider is now in charge of these issues. The cloud’s server architecture is accessible to businesses following their requirements, and they can save money by only paying for the cloud data they use.


Data has become more accessible because of cloud computing in a modern way. Smaller businesses may now undertake data analytics and compete in the market with more giant multinationals without being concerned about the excessive costs connected with data science. Data as a Service results from the growing popularity of data science and cloud computing (DaaS).


Popular Cloud Computing Platforms For Data Science

  • Amazon Web Services

A division of Amazon, Amazon Web Services is a cloud computing platform. It was introduced in 2006 and is today one of the most well-liked cloud computing platforms for data research. AWS offers a variety of tools for data analytics, including 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), among others. The relational database Amazon Aurora and the cloud-based database Amazon DynamoDB are among the database-related products offered by Amazon Web Services (NoSQL database). Netflix, NASA, and other well-known businesses use AWS.

  • Google Cloud

A cloud computing platform offered by Google is called the Google Cloud Platform. It gives businesses access to the same infrastructure that Google uses for its own internal products like Google Search, YouTube, Gmail, etc. BigQuery (Data warehouse), Dataflow (Streaming analytics), Dataproc (Running Apache Hadoop and Apache Spark clusters), Looker (Business Intelligence Analytics), Google Data Studio (Visualization Dashboards, Data Reporting), and Dataprep (Data Preparation) are just a few of the data analytics products offered by Google Cloud.

  • Microsoft Azure

Microsoft developed the cloud computing system known as Azure. It is a well-known cloud computing platform for data science and data analytics that was first introduced in 2010. Some of the Microsoft Azure products for data analytics include 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), etc. Microsoft Azure supports Azure SQL Database, Azure Cosmos DB, and other databases. 


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