Friday Jan 13, 2017

Are approximate answers the best way to analyze big data

Lots of candy

Image courtesy of pixabay.com

In my previous post I reviewed some reasons why people seem reluctant to accept approximate results as being correct and useful. The general consensus is that approximate results are wrong which is very strange when you consider how often we interact with approximations as part of our everyday life.

Most of the use cases in my first post on this topic covered situations where distinct counts were the primary goal - how many click throughs did an advert generate, how many unique sessions were recorded for a web site etc. The use cases that I outlined provided some very good reasons for using approximations of distinct counts. As we move forward into the era of Analytics-of-Things the use of approximations in queries will expand and this approach to processing data will become an accepted part of our analytical workflows.

To support Analytics-of-Things, Database 12c Release 12.2 includes even more approximate functions. In this release we have added approximations for median and percentile computations and support for aggregating approximate results (counts, median and percentiles)....

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The data warehouse insider is written by the Oracle product management team and sheds lights on all thing data warehousing and big data.

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