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My understanding is that data science implies big data.

As Jordan Tigani observes, there are not that many really big data situations[1].

Thus data science proficiency might be in support of business initiatives in a particular application domain and not a means to an end in itself.

[1]:https://motherduck.com/blog/big-data-is-dead/

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> My understanding is that data science implies big data.

It really doesn't.

I dont think OP is wrong -- the role popped up after 2007 Hadoop release. It was almost synonymous with running ML on big data and data scientists were typically computer science PhDs. It has become something quite different though. Now everyone and their dog is a data scientist.

Big data and data science became popular terms around the same time, doesn't mean that one implies the other like the GP comment suggested. Big data isn't a concern for most working data scientists today - not all datasets are that big, even cheap machines are very powerful and in the cases when you are working at huge scale a lot of the complexity is abstracted away from the data science work anyway.

so typically there are two sides of the house

1. data engineering - dealing with etl pipelines, data warehouses, data lakes, databases

2. data science - ai/ml, models etc

#1 preps things so #2 can do their jobs

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