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  2. A data mart is a data warehouse that serves the needs of a specific business unit, like a company’s finance, marketing, or sales department. On the other hand, a data lake is a central repository for raw data and unstructured data. You can store data first and process it later on.
    aws.amazon.com/compare/the-difference-between-a-data-warehouse-data-lake-and-data-mart/
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    What is the difference between data lake and data mart?A data warehouse gets its data from databases; a data mart gets its data from the data warehouse. The key differences between a data lake vs. a data mart include: Data lakes contain all the raw, unfiltered data from an enterprise where a data mart is a small subset of filtered, structured essential data for a department or function.
    What is the difference between data lake and data warehouse?Finally, in terms of users, a data warehouse is targeted toward decision makers at the middle and top management level, whereas a data lake requires a data scientist, which is a more specialized profile in terms of data handling and analysis.
    What is the difference between a data mart and data warehouse?The reason is because a data warehouse is structured and can be more easily mined or analyzed. A data mart, on the other hand, contains a smaller amount of data as compared to both a data lake and a data warehouse, and the data is categorized for a specific use or by a specific demographic or business unit.
    Are data lakes better than data marts?Data marts are very specific, allowing for fast, effective analytics of relevant summarized information. Data lakes are better for broader, deep analysis of raw data. Data lakes are more an all-in-one solution, acting as a data warehouse, database, and data mart. A data mart is a single-use solution and does not perform any data ETL.
     
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