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When discussing big data, it is sometimes mentioned that data modeling can be done by using a tool like map reduce, while data processing may be performed by apache spark. What is the difference between data modeling tasks, and data processing tasks? Thanks in advance

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  • Data modeling means representing the data, usually with a somewhat compact model. Modeling implies simplifications: this can lead to a good model which reliably represents the patterns in the data or a terrible model which simplifies too much or not enough.
  • Data processing is applying any kind of process to the data.

For the record, map-reduce is relevant only as a technique for processing large data efficiently.

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  • $\begingroup$ Could you refer a good site for learning modelling? $\endgroup$
    – Karl 17302
    Sep 23, 2022 at 4:43
  • $\begingroup$ @user17302 modelling is very specific to the domain, task, data. For example I'm doing NLP so I know techniques for representing text data, I know the type of features needed for different tasks in NLP, etc. That would be totally different for images, biomedical data... Basically you should choose which domain/application you're interested in imho. $\endgroup$
    – Erwan
    Sep 23, 2022 at 9:17

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