Data science and data engineering are two different branches of big data paradigm – an approach in which enormous speeds, varieties and volumes of structured, unstructured and semi-structured data are captured, processed, stored and analyzed using a set of techniques and new technologies compared to those used decades past.
Data Science and Data Engineering
In this article, I will compare Data Science and Data Engineering. Before comparing these fields on the basis of their profession, let’s start with understanding what both these concepts.
What is Data Science?
If science is a systematic way by which people study and explain domain-specific phenomena that occur in the natural world, then you can think of data science as a scientific field dedicated to the discovery of knowledge through the data-driven world.
Data scientists use mathematical techniques and algorithmic approaches to find solutions to complex business and scientific problems. Data science practitioners use their methods to obtain information that would otherwise be inaccessible.
Data scientists use mathematical techniques and algorithmic approaches to find solutions to both complex business and scientific problems. Data science practitioners use their methods to gain information that would otherwise be inaccessible.
In business, the goal of data science is to provide businesses and organizations with the information they need to optimize organizational processes for maximum efficiency and revenue generation.
What is Data Engineering?
If engineering is the use of science and technology to design and build systems that solve problems, you can think of data engineering as a field of engineering dedicated to overcoming processing bottlenecks and data processing issues for applications that use big data.
Data engineers use their computer and software engineering skills to design systems and solve problems in handling and processing large data sets.
Difference Between Data Science and Data Engineering on the Basis of Profession
The roles of data scientist and data engineer are often completely confused and intertwined by recruiters. If you look at the most job descriptions for companies that are hiring, they often don’t match titles and roles, or just expect candidates to do both data science and data engineering jobs.
If you hire someone to help you understand your data, be sure to define your needs very clearly before writing the job description. Since a data scientist must also have expertise in the particular field they are working in, this requirement typically prevents a data scientist from also having expertise in data engineering (although some data scientists have experience use of engineering data platforms).
And if you hire a data engineer who has data science skills, he or she usually won’t have much expertise outside of the data realm. Be prepared to call in a subject matter expert to help you.
So many organizations combine and confuse roles in their data projects, sometimes data scientists are stuck spending a lot of time learning how to do the job of a data engineer, and vice versa. To get the highest quality working product in the shortest time possible, hire a data engineer to process your data and a data scientist to understand it.
keep in mind that data science and data engineering are just two small roles within a larger organizational structure. Managers, mid-level employees and business leaders also play an important role in the success of any data-driven initiative. The main benefit of integrating data science and data engineering into your projects is to leverage your external and internal data to strengthen your organization’s decision support capabilities.
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