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Data Engineers vs. Data Scientists (Tools (Data Scientists: (Pandas +…
Data Engineers vs. Data Scientists
Fundamentals
Data Scientists may need to develop and ETL
ETL = Exchange, Transform, Load
Data Engineers might need to develop an API & front-end.
API = Application Programming Interface
Front-end = producing a graphical user interface for web digital use via HTML
Goals:
Data Engineers are much more focused
They build automated systems. Automated data structures.
Similar to other engineers: a lot of designing, assumptions, limitations, development needed to perform a final task.
Data Scientists are more question focused
Looking for ways to reduce costs/increase profits, improve customer UX, business efficiencies.
Question, hypothesize and conclude.
A vs. B testing
"Find an answer to whatever question is posed."
They analyze, gather support and can develop a conclusion to the question.
Tools
Both rely heavily on Python / R and SQL.
Python is a very robust language that has libraries that help manage operational tasks as well as analytical ones.
Data Scientists:
Pandas + Scikit Learn
Data Engineers:
Pipeline management ie. Airflow & Luigi