Mondrian Schema for OLAP Cube Definition ft. Google Analytics and Saiku

data-insightsWhat I am going to showcase in this tutorial is how to load web stats from Google Analytics into a fact table with Penthao Kettle/PDI. And then how to represent that fact table with Mondrian 3.6 schema so we can visualize the data with Saiku on Pentaho BI Server. In the end I’ll give my two cents on Saiku Analytics and possible options involving d3.js and Roland Bouman‘s xmla4js.

In case you are new to this I recommend reading my articles on the following topics involved here:

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Using the Dimension Lookup/Update Step in Pentaho Kettle

dim_lookup_update_iconIn a traditional star schema the dimensions are located within specialized tables which are referred to by numeric keys from the fact table. A dimension can represent anything from the gender (“male”, “female”, “intersex”) over a hierarchy representing a location (“Germany”, “RLP“, “Mainz“) to an individual user’s profile (name, address, date of birth, …). Now thanks to Mr. Kimball we know there are different types of what he refers to as Slow Changing Dimensions (SCD – “slow” because they are expected to change only infrequently):

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