Every business, even a small one like a mom-and-pop store, generates data through its interactions with customers and daily activities. That data has always been there, but we didn’t have the tools to record it, let alone pore through it later to unearth various insights and optimization opportunities. Nowadays, we have the tools and people who can handle this challenge.

The cloud provides the optimal combination of tools to handle big data. Data scientists and data engineers provide the expertise and know-how to derive relevant insights from big data through the power of the cloud.

What is the cloud, and how does it work? What does a data scientist do, and what is the link between data science and cloud computing? Let’s answer these questions.

What is Cloud Computing?

The cloud consists of a collection of technologies and technology services that reside on a distributed server system in a cloud environment.

Cloud computing translates to the delivery of various technology services through the cloud, featuring a pay-as-much-as-you-use pricing model. These characteristics give cloud computing a definitive edge over locally handled data storage and analysis.

The services available through cloud computing are:

  • Storage
  • Data processing
  • Networking
  • Databases
  • Software
  • Any other services and solutions users and providers can think of and implement

Through the cloud, companies can buy, deploy, and run big data systems and applications quickly and efficiently. For data scientists and engineers, the advantages of the cloud are game-changers.

Cloud computing solutions are several levels deep.

Infrastructure as a Service (IaaS)

When you implement a data storage and analysis solution on-premise, you handle every aspect of the operation and assume the inevitable costs of inefficiencies and redundancies.

You are fully responsible for:

  • Storage
  • Networking
  • Virtualization
  • Servers and server maintenance
  • Data
  • Applications
  • Runtime
  • OS
  • Middleware

An IaaS cloud solution takes the infrastructure off your hands, leaving you to handle the remaining components of your system. You no longer have to worry about storage, networking, virtualization, and server maintenance. The cloud takes care of that part of the work for you.

The IaaS approach is the equivalent of renting a car instead of owning one. You don’t have to buy a car and worry about maintenance, cleaning, and insurance. But you still have to fuel it, park it somewhere, drive it, and plan out your routes.

Platform as a Service

PaaS takes the cloud one step further, relieving end users of more worries and responsibilities.

In addition to storage, networking, virtualization, and servers, PaaS solutions also handle the OS, middleware, and runtime for you. All you need to cover is the data aspect of the operation and the applications you need to make it all work.

The practical equivalent of a PaaS setup is employing a ride-sharing service. Not only do you not have to buy the vehicle and worry about the basics, but you don’t even have to fuel it, secure parking space, or drive. All you need to handle is route planning.

Software as a Service

This type of cloud computing setup takes every operational aspect off the hands of the end-user. It takes care of everything, right down to the applications. The practical equivalent of a SaaS setup is taking the bus to work. You don’t own anything and don’t have to worry about anything. You pay for the solution, and you enjoy its benefits.

A public cloud service is what most people think of when they mention the cloud. Such services are available to all users, and many offer some free services. One can connect to a public cloud through the internet. Public cloud service providers are companies specializing in this type of activity.

Some companies maintain private clouds that work similarly to public ones, but only users within the organization can access them. The provider of the private cloud, in this case, the organization that maintains it, is responsible for administration, management, and the applications that end-users install. The provider pays all the costs associated with the private cloud.

Most private clouds use public cloud resources to operate. That makes them hybrid, public-private clouds.

What Advantages Does Cloud Computing Offer?

The advantages of cloud computing are obvious for those who use it to handle, manage, and process big data. These advantages make cloud computing a perfect match for data scientists and engineers.


Since users only pay for the cloud resources they use, cloud computing optimizes costs. Users don’t have to buy the hardware and software they would need to create an on-premise solution. And they don’t have to manage infrastructure on-site.


Cloud resources are permanently at-the-ready. Users can tap into them at a whim, and setup times are short. It may take only minutes to deploy a service.


To a layman, reliability may seem like an afterthought, but to a data engineer, it is an essential lifeline. Keeping systems reliable involves multiple backups and data centers preferably located far apart. Reliability also means disaster recovery plans and solutions, simulations, and planning for continuity. None of this is cheap, and cloud computing takes it all off your hands.


As a company grows, scaling becomes more and more of a challenge. Often, the costs it involves on the level of data science and engineering make it nearly impossible to scale up for small businesses. For a fast-growing company, server needs are unpredictable, even for the current quarter.

Cloud computing offers built-in scalability.

It allows users to add more resources when they need them and use less at other times. Since they only ever pay for what they use, cloud computing is the perfect solution for scaling.


Cloud providers constantly monitor the performance of their systems, adding resources whenever they deem it necessary. Service-level agreements guarantee certain performance levels, protecting users from unforeseen drops in performance.


Allocating and re-allocating resources is easy through the cloud. And it involves no extra costs.


Security is a concern for businesses that observe strict regulatory compliance since they put an external party in charge of security through the cloud. Cloud providers continuously increase the security of the solutions they offer.

What Does a Data Scientist Do?

Data scientists exist at the intersection of the IT and business worlds. They are mathematicians and statisticians who analyze data to discover patterns and insights that help their organizations evolve and optimize their activities.

A data scientist:

  • Tames seemingly chaotic data, turning it into an intelligible format
  • Uses techniques based on data to solve relevant business problems
  • Understands the mathematics behind statistics, distributions, and statistical testing
  • Is familiar with machine learning techniques, text analytics, and deep learning.
  • Knows how to use programming languages such as Python, R, and SAS
  • Plays the role of a bridge linking the IT and business departments
  • Is adept at spotting patterns and trends in data that may lead to conclusions and solutions that help the organization

To achieve their goals, data scientists use a selection of tools for which the cloud represents a natural and optimal environment.

  • Data scientists make sense of chaotic data through visualization. They process and compile data into intelligible charts and pictorials that make trends and patterns stand out even for the untrained eye.
  • Data engineers use machine learning and deep learning to transform data into a usable format. Machine learning uses automation and mathematical algorithms to create AI capable of performing the required functions.
  • Data scientists prepare data for consumption by compiling it into formats others can use.
  • Data scientists use pattern recognition solutions to spot relevant trends in data.
  • Text analytics solutions allow data scientists to pore through unstructured data to unearth relevant insights.

What is the Link between Data Science and Cloud Computing?

Now that you know what data scientists do, what they need, and what cloud computing gives them, it is easy to see that data science and cloud computing are a match made in heaven.

Companies like Walmart generate copious amounts of data from their customers every hour. More precisely, Walmart’s activity results in 2.5 PB of data per hour and 25 PB per day.

Without the benefits of cloud computing, data analysts would have to:

  • Manually retrieve the data
  • Turn their computers into single points of failure
  • Process the data at the speed the computing power of their local machine allows.
  • Handle the workload while working with a limited amount of data.

As you can see, it wouldn’t just be difficult to handle high-volume data without cloud computing. It would be downright impossible. No data scientists could leverage real-time data under these conditions and implement machine learning algorithms that run on such data.

With cloud computing, data scientists can do their work efficiently. They can:

  • Develop algorithms using real-time data if needed
  • Test hypotheses
  • Take full advantage of all available data
  • Be efficient without having to wait hours for the results of their tests and simulations.
  • Not worry about the availability of local data processing resources

In the corporate world, cloud-based data science and cloud computing are great equalizers. They grant small companies access to resources and data only the largest corporations could access in the past. Cloud technologies democratize data science and engineering. They give users access to pre-installed open-source frameworks instantly, without the need to go through an installation process. Data scientists can thus use a wide range of cloud-based applications. And if they need more processing power, they can always pay more to speed things up.

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