Question
Data scientists often work with large, evolving datasets. Explain the limitations of traditional file-based systems in the context of analytics and machine learning workflows. How does a DBMS address these limitations?
Answer :
Word Count : 266
Data scientists frequently handle massive, continuously growing datasets that require efficient storage, retrieval, integration, and analysis. Traditional file-based systems, where data is stored in separate flat files, present several limitations in analytics and machine learning workflows. One major issue is data redundancy and inconsistency. Since files are maintained independently, the same data may ____ _______ ____ _____ _______ _______ __________ _______.
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Data scientists frequently handle massive, continuously growing datasets that require efficient storage, retrieval, integration, and analysis. Traditional file-based systems, where data is stored in separate flat files, present several limitations in analytics and machine learning workflows. One major issue is data redundancy and inconsistency. Since files are maintained independently, the same data may ____ _______ ____ _____ _______ _______ __________ _______.
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