DATABASE FUNDAMENTALS
DATA WAREHOUSING AND DATA MINING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It aggregates data to make it ready for further use
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It is flexible for building data warehouse
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It is suitable architecture for big data
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It is suitable for ELT approach
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Detailed explanation-1: -The separation allows your business to archive raw data on less expensive tiers while allowing faster access to transformed, analytics-ready data. Being able to run experiments and exploratory analysis with new technologies is much easier thanks to such data preparation.
Detailed explanation-2: -Data Lake Benefits Because the large volumes of data in a data lake are not structured before being stored, skilled data scientists or end-to-end self-service-bi tools can gain access to a broader range of data far faster than in a data warehouse.
Detailed explanation-3: -Extract, Load, Transform (ELT) is a data integration process for transferring raw data from a source server to a data system (such as a data warehouse or data lake) on a target server and then preparing the information for downstream uses.
Detailed explanation-4: -ETL vs ELT ETL is the legacy way, where transformations of your data happen on the way to the lake. ELT is the modern approach, where the transformation step is saved until after the data is in the lake. The transformations really happen when moving from the Data Lake to the Data Warehouse.
Detailed explanation-5: -ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself. ETL does not transfer raw data into the data warehouse, while ELT sends raw data directly to the data warehouse.