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ETL = the “make the data usable” workflow \t•\tExtract: Pull raw data from places like CRMs, product logs, spreadsheets, APIs, or databases. \t•\tTransform: Clean and standardize it (remove duplicates, fix formats, handle missing values, normalize categories). This is where “garbage in” becomes “usable in.” \t•\tLoad: Put the cleaned data into the destination that powers analytics or AI—like a data warehouse, lake, or feature store. Why ETL matters for AI AI models don’t learn from “raw chaos.”
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