Imagine a small company that wants to use data analytics to improve its services and gain an edge over its competitors. This company generates some data but it also accesses data from third-party sources to obtain insights. But the question is how to take advantage of this data? After all, this small company is no Google or Facebook! It doesn’t have the resources or financial capabilities to store large amounts of data in local servers for data analysis. So Cloud Computing acts as the savior! Even before this company can use Data Science, it has to focus on Cloud Computing first.
But are you wondering what is the role of cloud computing in this? How is it important for Data Science? We’ll get to that in this article but first, let’s see what cloud computing is!
What is Cloud Computing?
Cloud computing allows companies to access different computing services like databases, servers, software, artificial intelligence, data analytics, etc. over the internet, which is called the cloud in this case. These companies can run their applications on the best data centers in the world with minimal costs. This also ensures that small companies or those in emerging economies can use this technology for ambitious and complex projects that would otherwise be quite costly. And this is true in the domain of Data Science as well. Cloud Computing has made Data Analytics and Data Management much simpler for Data Scientists. Let’s see how!
Why is Cloud Computing Important in Data Science?
Let’s imagine for a second that there was no Cloud Computing for Data Science. Then companies would have to locally store data in servers and every time a Data Scientist needed to perform data analysis or extract some information from the data, they would need to transfer the data to their system from the central servers and then perform the analysis. Can you imagine the complications in this?! This is not just a little bit of data as data analysis by companies uses a huge volume of data.
Moreover, it is very expensive to create servers for the data and while big companies can manage this easily, it is very different for the smaller companies. These smaller companies cannot use servers as they require space to keep them. These servers require constant maintenance and upkeep and also require backups in case anything goes wrong. Having servers also require immense planning and it may also happen that companies may obtain more or fewer servers than they need according to their data requirements. And this is where cloud computing comes in! Companies can use the cloud to host their data and they don’t need to worry about servers anymore as this is the headache of the cloud provider now! The companies can access server architecture in the cloud according to their needs and even save money by only paying as much as the data they are using on the cloud.
Cloud computing has democratized data in a manner that is unique in these times. Now, smaller companies can perform data analytics and compete with larger multinationals in the market without worrying about the insane costs associated with Data Science. In fact, Data Science with Cloud Computing has become so popular now that it has given birth to Data as a Service (DaaS).
What is Data as a Service?
Data as a Service(DaaS) is becoming a popular concept with the advent of cloud-based data services. DaaS is provided by data vendors that use cloud computing to provide data storage, data processing, data integration, and data analytics services to companies using a network connection. Hence, Data as a Service can be used by companies to better understand their target audience using data, automate some of their production, create better products according to market demand, etc. All of these things in return increase the profitability of a company which in turn gives them an edge over their competitors.
Data as a Service is similar to Software as a service, Infrastructure as a service, Platform as a service, etc. which are all common services that everyone has heard of in the tech world. However, DaaS is comparatively new and gaining popularity only now because of the increasing need for Cloud Computing in Data Science. But Daas is increasing in popularity now because of the fact that basic cloud computing services provided by companies were not equipped initially to handle the massive data loads that are a necessary part of DaaS. Instead, these services could only manage basic data storage rather than data processing and analytics on such a large scale. Also, it was difficult to manage large data volumes over the network earlier as the bandwidth was limited. However, these things have changed with time and now, low-cost cloud storage and increased bandwidth have made Data as a Service the next big thing!
In fact, it is estimated that DaaS will be used by around 90% of large companies in order to generate revenue from data by 2020. Data as a Service will also allow different departments in large companies to share data easily with each other and obtain actionable insights even if they don’t have the data infrastructure in-house to manage this feat. Therefore, DaaS will make sharing data for companies much easier and faster in real-time, which will, in turn, increase the profitability of a company.
Cloud Computing Platforms For Data Science
1. Amazon Web Services
Amazon Web Services is a cloud computing platform that is a subsidiary of Amazon. It was launched in 2006 and is currently one of the most popular cloud computing platforms for data science. AWS provides various products for data analytics which include Amazon QuickSight (business analytics service), Amazon RedShift (data warehousing), AWS Data Pipeline, AWS Data Exchange, and Amazon Kinesis (real-time data analysis), Amazon EMR (Big data processing), etc. Amazon Web Services also provides products for databases which include the Amazon Aurora (relational database) and Amazon DynamoDB (NoSQL database). Some of the more popular companies that use AWS include Netflix, NASA, etc.
2. Google Cloud
The Google Cloud Platform is a cloud computing platform that is provided by Google. It provides the same infrastructure for companies that Google itself uses in its internal products such as Google Search, YouTube, Gmail, etc. Google Cloud provides various products for data analytics which include 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), etc.
3. Microsoft Azure
Microsoft Azure is a cloud computing platform created by Microsoft. It was initially released in 2010 and is a popular cloud computing platform for data science and data analytics. Some of the Microsoft Azure products for data analytics are 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 also has support for databases including Azure Cosmos DB (NoSQL database), Azure SQL Database (SQL database), etc.
