When it comes to data science vs data engineering, Both of them are the most demanding careers of the 21st century. Before answering this question about data science vs data engineering, go through them below as I have mentioned.
A Data Scientist builds models using mathematics, statistics and machine learning to explain and predict complex behavior, and codifies those models into real-world software. A Data Engineer designs and builds data architectures for ingestion, processing, and surfacing data for large-scale data-intensive applications.
Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale.
As with the Data Scientist there is no formal path to becoming a Data Engineer since it is a unique blend of skills that have been brought together to form a distinct and much needed discipline. The requirements for a Data Scientist are typically more “academic” as they are expected to understand and conduct scientific research and know how to build and test advanced models. PhDs are often sought for Data Science, with backgrounds in the hard sciences or computer science. Data Engineers typically come from an engineering background with less emphasis on the academic background, although many still have Masters degrees. Often developers interested in designing large scale architectures for data-intensive applications can move towards this field as there is much less emphasis on science and math and more on engineering and development.
Data Engineers should understand the core concepts in computer science and should be very well versed in building and designing large scale applications; end-to-end. They should understand the pros and cons of using relational and noSQL databases. They must know how to design effective pipelines for both batch and streaming use cases. They must know what it takes to operationalize a working model and how to help push some of the “lab” specifics (training and validation) into real-time engines. They must understand distributed computing and should be able to work with the Data Scientist to help split algorithms effectively to still yield predictive accuracy across a variety of domains. They should know when to push schemas towards the application to allow for “data lake” designs that assist in large scale analysis but still serve domain-specific applications. And they should be very familiar with the core technologies that are used to build these systems.
I Guess you might have a clear insight about data science vs data engineering.