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Contrary to popular belief, and much to the dismay of big-data developers, OLAP - the high(er)-speed data data block of old, far from refuses to die, but instead, appears to be growing in strength.
From a period where data-space was a premium, data processing was costly and analytical skill was scarce OLAP (Online Analytical Processing) was born, devised and thus defined as pre-aggregated chunks allowing users to simply drag-and-drop these pre-aggregated pieces into a pivot-table in order to very quickly analyse huge amounts of data (in the millions - I know, not so huge any more), and then, to enhance the output with charts, this was seen at the start of the early 2000's as the ultimate in performance solutions; looking at the basics of the architecture, you could be forgiven for noticing that Tableau behave's behaves in a very similar way.
But, as space is no longer such a premium, skills lie in abundance and most smartphones are built with more space and power than servers of old (even if they only use this hardware for facebook Facebook and playing snake), still, OLAP refuses to be dropped quietly and, whats what's more, as big data has really taken-off, OLAP is seeing a sort of revival as teams seek to compress massive data-sets into more user-friendly outputs for Microsoft Excel and to a lessor degree, OpenOffice Calc and/or GoogleSheetsGoogle Sheets, it is becoming ever clear that OLAP means to stay. Which can present a problem when cubes are seen as the single-version-of-the-truth, and reports are cube-centric.
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