Do you know that Walmart, the retail giant of the United States, generates 2.5 petabytes of data from their customers every hour? Do you know what a petabyte is? It’s equal to 1 million gigabytes which is similar to 13.3 years of HD video.
That means in a single day, Walmart generates about 25 petabytes of data, which is about 130 years of HD video.
There is no doubt that most businesses are not as big as Walmart. However, these days, even the smaller businesses are generating a significant amount of data daily. Indeed, the abundance of this data can make anyone pretty confused. It is the data scientists who handle this enormous data and help us to navigate through it.
It can be easily said that data science is in the heart of it all. However, before we dip into the wide sea of data science, we should pay due diligence to another important player in this scenario; the cloud and cloud computing.
In this article, we will try to focus on the relationship between these two. Let’s dive in.
Why Cloud Computing Is Necessary for Data Science?
To understand the benefits that cloud computing extends to data scientists; we have to imagine a different world. In that imaginary world, there are no servers available to us; however, we have to handle just as much data as these days.
In this type of scenario, what would the firms do? They will try to get access to the databases that run locally, right? That means, whenever the data scientists want to improve an existing algorithm or engage in a new analysis, they have to transfer the information from the central database to their machines. Once the transfer is completed, they will operate on the information locally.
While it might seem pretty easy, there are certain drawbacks that the data scientists have to face.
- They have to intervene manually for retrieving data.
- Their machine becomes the single point of failure for the analyses you have operated on locally.
- The processing speed of the data would be equal to the computing capacity of their computers.
- The chances are they will be forced to work on a limited amount of data as they will be dependent on the limited computing resources.
Also, they will not be able to leverage the real-time data to create the recommender system or any other type of machine learning algorithms that require the intervention of live data.
This does not look good, does it?
Now Comes The Servers
The above scenarios are somewhat responsible for the invention of the servers. However, the servers come with the drawbacks of their own.
- The most prevalent problem is that the server requires a place to be stored.
- The infrastructure for the servers is expensive to buy and set up.
- The in house data storage system requires you to have back-ups in different locations across the earth.
- Businesses require to plan for using the server spaces. More often than not, they end up buying more servers than they require.
The Benefits of Clouds
Now it would be easy for you to understand the benefits of clouds. They tend to overshadow the local servers in every possible way. They make sure that the data scientists only focus on developing and testing new algorithms while reaping the benefits of all the available data. Cloud computing helps the data scientists to work on their projects without waiting for hours and worrying about the available memory space of their computers.
Even though the data scientists have to wait for a long time sometimes, however, they always have the option to pay more and get the job done faster. This is yet another advantage that cloud computing extends to data scientists. Irrespective of the size of the organizations, the scientists get to access the same tools without paying a huge sum of money. For the data scientists across the work, the cloud services have become a huge enabler.
Cloud computing helps the data scientists to use the platforms like Windows Azure that offer access to different tools, frameworks and programming languages for a fee as well as for free. With the help of cloud computing the data scientists can use open source tools like R, Python, and other scalable machine learning tools as well as the commercially available ones like MS SQL, Oracle RDB, BusinessObjects etc.
The size of the data sets and the availability of the platforms and tools make it essential for data scientists to understand cloud computing.
All in all, cloud computing has democratized data science and data analysis for all data scientists across the world. The simple fact that the data analysts and scientists can rely on the data stored on the cloud makes their life so much easier. The biggest selling point of cloud machine learning is that it enables the medium and small enterprises to access the machine learning infrastructure that they otherwise would not be able to afford.
Thanks to cloud-based machine learning, even the smallest e-commerce retailer can run a real-time recommended system for improving their customer services. With the help of the technology, they can analyze the intention of the customers and extend personal help to the. All they need is a good data scientist to enable these benefits for them. If you are thinking about maximizing the revenues of your business, don’t wait any more. Get in touch with our experienced data scientists right away.