There’s a fair amount of confusion about how best to collect, integrate, and preprocess data for the purposes of advanced analytics. Many business intelligence and data warehouse professionals think it’s the same as the traditional ETL practices they have applied to their report-oriented data warehouses for years. And some database administrators think it’s just a matter of dumping large volumes of data into a highly scalable repository. Somewhere between these two are emerging best practices for preparing data for advanced analytics. That’s because the transformational processing of ETL alters source data in ways that can expunge the data nuggets that successful analyses depend on. At the other extreme, merely copying data won’t put you in a position to get the most value from the integrated data.
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