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We define agile analytics as the iterative and rapid processing of data for decision support. There is minimal upfront investment. Developers instead process source data and build queries incrementally as business needs emerge.
Big Data and data warehouses are both technologies for realizing agile analytics. They differ in several ways.
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Few database projects start with a clean slate. Many operational applications have legacy databases as a source of data and ideas. Analytical applications have the operational databases that are feeding data. Developers often encounter existing databases that are poorly documented and need to figure them out. We use the term database archaeology to refer to the study of database artifacts.
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