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  2. Extract, Load, Transform (ELT) is a data integration process that involves the following steps:
    1. Extract: Raw data is extracted from a source system.
    2. Load: The data is loaded into a destination resource, such as a data warehouse.
    3. Transform: The data is transformed as needed12345.
    Learn more:
    ELT, which stands for “Extract, Load, Transform,” is another type of data integration process, similar to its counterpart ETL, “Extract, Transform, Load”. This process moves raw data from a source system to a destination resource, such as a data warehouse.
    In computing, extract, transform, load (ETL) is a three-phase process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collated from one or more sources and it can also be output to one or more destinations.
    en.wikipedia.org/wiki/Extract,_transform,_load
    Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).
    Extract, Load, Transform (ELT) is the process of first extracting data from different data sources, then loading it into a target data warehouse, and finally transforming it. ELT has emerged as a paradigm for how to manage information flows in a modern data warehouse.
    ELT stands for Extract, Load, and Transform. It is a type of data integration process used to transfer and manipulate raw data from a source system to a target system, such as a data lake or data warehouse. ELT is the inverse of the ETL (Extract, Transform, Load) method, which transforms data before loading it into the target system.
    research.aimultiple.com/what-is-elt/
     
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