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In today’s world, two major forces have emerged that are changing the rules of the game:
Data science and cloud computing.
Imagine a world where colossal amounts of data are generated every second.
Well… you don’t have to imagine it… It’s our world!
From social media interactions to financial transactions, from health records to e-commerce preferences, data is everywhere.
But what good is this data if we can’t get value?
That’s exactly what data science does.
And where do we store, process and analyze this data?
That’s where cloud computing shines.
Embark on a journey to understand the intertwined relationship between these two technological wonders.
Let’s try) to discover it all together!
Data science? -? The art of extracting knowledge
Data science is the art and science of extracting meaningful insights from vast and varied data.
It combines expertise from multiple domains, such as statistics and machine learning, to interpret data and make informed decisions.
With the explosion of data, the role of data scientists has become paramount in turning raw data into gold.
Cloud Computing? -? The digital storage revolution
Cloud computing refers to the provision of on-demand computing services over the Internet.
Whether we need storage, processing power, or database services, Cloud Computing offers a flexible and scalable environment for businesses and professionals to operate without the overhead of maintaining physical infrastructure.
However, most of you must be thinking why are they related?
Let’s go back to the beginning…
There are two main reasons why cloud computing has become a fundamental or complementary component of data science.
#1. The urgent need to collaborate
At the beginning of their data science journey, junior data professionals typically start by setting up Python and R on their personal computers. They then write and run code using a local integrated development environment (IDE) such as the Jupyter Notebook app or RStudio.
However, as data science teams expand and advanced analytics become more common, there is increasing demand for collaborative tools to deliver insights, predictive analytics, and recommendation systems.
That is why the need for collaborative tools becomes paramount. Essential for insights, predictive analytics, and recommendation systems, these tools are augmented by reproducible research, portable tools, and code source control. The integration of cloud-based platforms further amplifies this collaboration potential.
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It’s critical to note that collaboration is not just limited to data science teams.
It encompasses a much broader range of people, including stakeholders such as executives, departmental leaders, and other data-focused roles.
#2. The era of big data
The term Big data has gained popularity, particularly among large technology companies. While its exact definition remains elusive, it generally refers to data sets that are so vast that they exceed the capabilities of standard database systems and analytical methods.
These data sets exceed the limits of typical software tools and storage systems in terms of capturing, storing, managing and processing data in a reasonable period of time.
When considering Big Data, always remember the 3 Vs:
- Volume: It refers to the large amount of data.
- Variety: Point out the various formats, types and analytical applications of data.
- Speed: Indicates the speed at which data evolves or is generated.
As data continues to grow, there is an urgent need for more powerful infrastructures and more efficient analysis techniques.
So these two main reasons are why we as data scientists need to scale beyond local computers.
Instead of owning their own IT infrastructure or data centers, businesses and professionals can rent access to anything from applications to storage from a cloud service provider.
This allows businesses and professionals to pay for what they use when they use it, rather than dealing with the cost and complexity of maintaining your own on-premises IT infrastructure.
To put it simply, Cloud Computing is the delivery of on-demand computing services, from applications to storage and processing power, typically over the Internet and on a pay-as-you-go basis.
As for the most common providers, I’m pretty sure you all know at least one of them. Google (Google Cloud), Amazon (Amazon Web Services) and Microsoft (Microsoft Azure are the three most common cloud technologies and control almost the entire market.
The term cloud It may seem abstract, but it has tangible meaning.
At its core, the cloud is about networked computers sharing resources. Think of the Internet as the largest computer network, while smaller examples include home networks like LAN or WiFi SSID. These networks share resources ranging from web pages to data storage.
In these networks, individual computers are called nodes. They communicate using protocols such as HTTP for various purposes, including status updates and data requests. Often these computers are not on site but in data centers equipped with essential infrastructure.
With the affordability of computers and storage, it is now common to use multiple interconnected computers instead of one expensive power station. This interconnected approach ensures continuous operation even if a computer fails and allows the system to handle higher loads.
Popular platforms like Twitter, Facebook, and Netflix exemplify cloud-based applications that can handle millions of daily users without crashing. When computers on the same network collaborate for a common goal, it is called cluster.
Acting as a single unit, clusters offer improved performance, availability, and scalability.
Distributed computing Refers to software designed to use groups for specific tasks, such as Hadoop and Spark.
So…again…what is the cloud?
Beyond shared resources, the cloud encompasses servers, services, networks and more, managed by a single entity.
While the Internet is a vast network, it is not a cloud, as neither party owns it.
In short, data science and cloud computing are two sides of the same coin.
Data Science provides professionals with all the theory and techniques necessary to extract value from data.
Cloud Computing is what provides the infrastructure to store and process this same data.
While the first gives us the knowledge to evaluate any project, the second gives us the feasibility to execute it.
Together they form a powerful tandem that is promoting technological innovation.
As we move forward, the synergy between these two will strengthen, paving the way for a more data-driven future.
Embrace the future, because it’s data-driven and cloud-powered!
Joseph Ferrer He is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of Data Science applied to human mobility. He is a part-time content creator focused on data science and technology. You can contact him at LinkedIn, Twitter either Half.
Joseph Ferrer He is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of Data Science applied to human mobility. He is a part-time content creator focused on data science and technology. You can contact him at LinkedIn, Twitter either Half.
