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 :
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Skip in 10
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 be stored in multiple locations, leading to duplication and potential inconsistencies when updates are not synchronized. This directly affects data quality, which is critical for accurate machine
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