Both Data Science and Cloud Computing have been on the rise since the start of the 21st century. With the ability to now handle petabytes worth of data every single, it is important that you understand how this much of data can be effectively stored on a cloud platform. These days, using the cloud technologies to do processing has also been used effectively.
In this blog, we will be taking a look at the following concepts:
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Let’s begin!
Did you know that the retail giant Walmart generates around 3 petabytes of data every single hour? One petabyte is the equivalent of storing around 14 years’ worth of High Definition videos.
All in all, the exponential rise in the amount of data the world generates has given forth to expert Data Scientists, Data Analysts, and Data Engineers who are more than adept to handle whatever you throw at them!
Importance of Data Science with Cloud Computing
With the advent of Cloud Computing, followed by the dawn of the exponential use of Data Science, we are now faced with immense amounts of data that have to be stored, maintained, and analyzed. A cloud environment is a perfect architecture to effectively do this.
Why cloud, you ask? Every time a Data Scientist wants to perform a new set of analytics or to work on refreshing an algorithm, he/she would have to move the data to the local machine from the database and then proceed to work on it. Now, think about moving the petabytes of data to your home/work PC every day again and again. Doesn’t make sense, right? This is the challenge the world of Cloud Computing aims to solve.
Numerous companies across the globe have spanned their business units and models to scale to the clouds. This provides numerous advantages to both the organization and the developers alike.
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To understand the job of a Data Scientist in the cloud domain, you must first understand the types of data that are likely to be used in the cloud:
- Structured Data
E.g.: Addresses, geolocation, stock information, etc.
- Unstructured Data
E.g.: Emails, images, videos, social media messages, etc.
Even though data storage is getting cheaper and cheaper as the days pass, the data handling part still needs to be handled. In the world of Cloud Computing, data is stored across servers and networks all around the globe. Working with the plethora of tools and frameworks available to handle all this becomes key.
Data Scientists Using the Cloud
There is no doubt when one says that Cloud Computing has revolutionized the world of computing as we know it. Powerful platforms such as AWS, Azure, and others are taking over the industry by providing users with lots of tools and techniques to easily handle vast varieties of data.
When handling data, Data Scientists work with many tools, such as MapReduce, Hadoop, Pig, Hive, Mahout, and more, and languages, such as Java, C#, and Python, to comfortably carry out their tasks.
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Most of the tools that the Data Scientists use, be it in the cloud domain or the local environments, are open source. Following are some of these tools:
- R
- Python
- Hadoop
- MS SQL
- Tableau
- OracleDB
Now, there are obvious advantages of using a cloud server architecture:
- Storage is handled by the owner of the server.
- The server infrastructure is already built to scale.
- Clouds offer data backups and secure storage.
- Pay-as-you-go policies are light on the pocket.
We can talk about the advantages of Cloud Computing all day long, but I am sure you got the point here.
Think about this. Training a complex algorithm can take hours together at a time, but then with the cloud, you just pay more and get this job done faster.
Knowing the importance of Cloud Computing in the world surrounded by vast amounts of data, it becomes extremely vital for a Data Scientist to understand how to use Cloud Computing to the fullest.
Some of the easiest ways to go about working with cloud environments, alongside Big Data, are as below:
- Begin by installing Java or Python
- Install Scala or PySpark
- Install other frameworks and tools
- Start learning them
If working in a local environment, you will have to install these tools and frameworks. But, if you’re using a cloud service, these are ready to be used right away, already installed and configured to work well!
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What is Data as a Service(DaaS)?
The concept of Data as a Service (DaaS) is gaining traction, particularly with the emergence of cloud-based data services. DaaS, facilitated by data vendors utilizing cloud computing, offers an array of services, including data storage, processing, integration, and analytics to enterprises via a network connection. This service model is instrumental for companies aiming to:
- Gain deeper insights into their target audience through data analysis.
- Automate certain production aspects.
- Tailor products in alignment with market demands.
The resultant benefits are many, notably enhancing a company’s profitability and providing a competitive advantage.
Much like the widely recognized services – Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS), DaaS is carving its niche in the tech domain. The rising importance of cloud computing for data science is propelling DaaS into the spotlight. Initially, the basic cloud services were ill-equipped to handle the voluminous data integral to DaaS, being limited to basic data storage rather than extensive data processing and analytics. Moreover, the limited network bandwidth posed challenges in managing large data volumes.
However, the narrative has shifted with the advent of low-cost cloud storage and enhanced bandwidth, positioning Data as a Service as the next significant advancement. It’s projected that by 2020, approximately 90% of large enterprises will utilize DaaS to monetize data. This service model facilitates smooth data sharing across various departments within large organizations, even in the absence of in-house data infrastructure, enabling:
- Real-time data sharing, which is expedient and efficient.
- Gathering actionable insights, and facilitating informed decision-making.
The evolution of DaaS is not only a testament to the advancements in cloud computing but also a reflection of how integral data has become in driving business success. Through DaaS, companies are now better equipped to navigate the data-centric landscape, ensuring profitability and a robust competitive stance in the market.
Future of Data Science and Cloud Computing
In a real-world scenario, the following points add so much weight to the usage of Cloud Computing:
- Easy-to-access infrastructure: It can drive any sort of data analysis and powerful algorithms with ease.
- Small-medium-sized enterprises: It provides immense benefits in terms of affordability and ease of use.
There is a reason why companies, such as Pfizer, Shazam, NASA, Nokia, Netflix, Airbnb, and thousands of others, have completely moved toward using a cloud platform for their daily needs.
With faster storage methodologies, powerful tools, and new frameworks, the future path seems quite interesting and clear to a Data Scientist as to how Cloud Computing and Data Science can change the world for the better.
With the rise of Data Science and around 28 billion devices being connected to the Internet, the advent of powerful computation using cloud services is now in full effect!
Prepare yourself for the industry by going through Top Data Science Interview Questions and Answers now!
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