With each passing day, internet users generate more than 2.5 quintillion bytes of data in all possible forms. Whether in text, speech, images, videos, queries, or digital footprints, the data generated by users is crucial to organizations. But how?
What you search on online shopping platforms, their prices, your preferences act as a basis on which related products and advertisements come up on your screen. The data on what you stream on online streaming platforms play the part of a backbone to give you personal recommendations in the future. And the type of posts you view, like, and share on the social media platforms will encourage the in-built AI and ML to sort out various posts and arrange them in your feed to keep you engrossed.
The motive is simple: to give you what you like, enabling the companies to generate the maximum revenue. To make a recommendation, the first and foremost requirement is the user data. Here is where the role of data engineers and data scientists shows up.
Data Engineers: What do they do?
Data engineers are the professionals who work with raw data. It is nothing but the data obtained from the network, as it is, generated by the users. After getting their hands on the raw data, they collect the relevant data, sort them according to preferences, and develop infrastructures and architectures to store them for further usage.
The infrastructures can be in the form of databases or processing systems, using which accessing the already sorted relevant data becomes less tiring. In short, data engineers are the ones who scratch through raw data, sort and extract the relevant ones, improve reliability and increase data efficiency. After the filtration and corrective alterations, they are expected to present the data in readable format for further operations.
What does a Data Scientist do?
After the said processes are complete, the data scientist comes into the picture and utilizes the already developed platform by the data engineers. The data obtained by data scientists are purified to some extent, which acts as a source feed for multiple purposes like AI scaling, ML, data modeling, and prediction.
Data scientists are often entrusted with tasks such as using the data to conduct high-level market research. Identifying flaws and progresses in the strategies and devising plans also count as a part of their responsibilities.
After proper strategies get printed on paper, figures represented in human-readable and easily understandable form, flaws and progressed mapped out, preferably in graphs and charts, the data scientists are then required to send the complete report after every fixed period to the top decision-makers of the firm. The chronicle then forms the basis of the growth path that the organization will follow till further reports get delivered by the team of scientists.
Conclusion:
Data Scientists and Data Engineers, often confused as the same, are complimentary but not contradictory either. While the latter professionals sort out the raw material from a pile of materials, the other takes those raw materials and gives them an understandable shape. Hand in hand, the data scientists, the data engineers, and the decision-making committee of an organization are responsible for steering the firm towards a positive direction; based on what the facts and figures portray.
