What 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:
Pentaho’s BI Server or BA platform allows you to access business data in the form of dashboards, reports or OLAP cubes via a convient web interface. Additionally it provides an interface to administer your BI setup and schedule processes. The aim of this tutorial is to illustrate how to get started with the BI Server and for that purpose I am going to use a small and artifical data set – as otherwise I would have to get deeper into further technologies – so I can keep this text lean. I am going to elaborate on Mondrian schemas, data warehouse design, MDX and further related concepts in separate articles. When you are through with this text and still hungry, make sure you check out “Mondrian Schema for OLAP Cube Definition ft. Google Analytics and Saiku” for a more advanced use case.