Posted by: Dave | January 25, 2009

The Data Cube

Okay, first blog post here. Seems fitting that I should touch on what is a data cube.

A Data Cube is a data structure used in Business Intelligence and Analytics to allow for fast analysis of data.

Okay, sounds great, right? But what does that mean? A Data Cube is a data structure specifically designed to provide large amounts of information quickly, especially for visual display.

But isn’t that what my database if for? No, not exactly. A [relational] database is great at recording very granular data in no particular order. A data cube contains data that has been collected and is ready for immediate display in a table, chart, or graph. 

How is that really any superior to a database, I still don’t get it. I mean, it sounds like we’re just pulling data from the database and putting it in a temporary location before it’s rendered as a graph. Why not exclude the middle man and go straight from database to graph? This is the beauty of the data cube. A data cube is called a cube because it is multidimensional. A cube is 3D, but a ‘Data Cube’ may be as many dimensions as you want.  Three example dimensions of a cube might be { [Products], [Cities], [Time] }.  You may quickly view data as Product Sales per Quarter, Product Popularity by City,  or Sales per City per Quarter. You may also clump data together, or break it down: view data by country instead of by city, or view by month instead of by quarter. 

Viewing the data from different angles like this would still be possible from a database – but queries would take longer to run, databases could suffer performance drops, and changing your point-of-view of data would be a much more painful experience. 

Wow, sounds great. In summary, a data cube is a data structure storing data in multiple dimensions, allowing for quick analysis of the data from different angles. The data cube may be used as a source of data for reports, graphs, charts, or casual curiosity.



  1. I used to use a Rubik’s cube analogy – both to stand for the multidimensional aspect you reference and also to introduce the idea of moving data around in different planes to look at it how you want and maybe obtain new insights.

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