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The process of extracting, loading, and transforming allows faster transfer of source data. Data Integration: Combining data from different sources into a single, coherent view. Rather than working with multiple data sources and dealing with incompatibility between systems, ETL allows businesses to leverage integrated data collections and manageable data flows. It ensures the integrity of the data to be used for reporting, analysis and prediction with machine learning models. These machine learning models analyze data after it has gone through an ETL process and then make predictions based on that data. ETL helps businesses by extracting data, transforming it, and then loading it into databases linked to machine learning models. It is a process that combines data from different sources into a single warehouse so that it can be processed and then analyzed to obtain useful information. Besides reformatting data, there are other reasons why data needs to be transformed. Streaming Incremental Loading: Data is loaded at intervals, usually daily.

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