Bhubaneswar: The debate over data science roles is nothing new; companies’ increased focus on acquiring data science talent has made the role so sought-after.
Recently, a lot has been written about the difference between data scientists and data engineers. Maybe the surge in interest comes from the fact that there indeed has been a change in perspective over the years. A couple of years ago, the focus was more on retrieving valuable insights from data.
The key difference between data engineers and data scientists is data engineers transform data into a format that is ready for analysis. These professionals are usually software engineers.
Their job involves cleaning the data, compilation and installation of database systems, scaling to multiple machines, writing complex queries, and strategising disaster recovery systems.
Data scientists usually start with data pre-processing, which is cleaning, understanding, and trying to fill gaps in the data with the help of domain experts. Once this is done, they will build models which are truly valuable in extrapolating, analyzing and finding patterns in existing data.
Basically, the role of a data scientist has been projected as a must-have entity for all disruptive technology projects. The data scientist mainly focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes.
On the other hand, a data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems.
We can say that data scientists dig into the research and visualization of data, whereas data engineers ensure data flows correctly through the pipeline. Both are very essential and have a tremendous demand in the market with limited supply. It all depends on individual interests and strengths whether to become a data scientist or a data engineer.
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