A Simple Date Dimension for Mondrian Cubes

cube

Date dimensions are among the most important dimensions of many Mondrian cubes. The usefulness of a cube often depends on the way the date dimension has been modeled. This post shows how to create a basic date dimension and how it can be augmented with properties to suit specific analysis needs. If at some point you . . . → Read More: A Simple Date Dimension for Mondrian Cubes

A Basic Mondrian Cube: Using Multi-Level Dimensions

acme_cube

The previous post explains how to create a star schema for a Mondrian cube. The dimensions in the example data were very simple. Each dimension had only a single field whose value ended up being a member in the cube. In this post I would like to build a cube that has dimensions with multiple levels. . . . → Read More: A Basic Mondrian Cube: Using Multi-Level Dimensions

A Basic Mondrian Cube: Introducing the Star Schema

star_schema

The previous post explained how to create a simple Mondrian cube. It is meant to provide the most basic information on how Mondrian cubes are created and stored. The previous solution uses only one DB table to store the entire cube. It uses only degenerate dimensions, to use Mondrian parlance.
This article builds on the basics already . . . → Read More: A Basic Mondrian Cube: Introducing the Star Schema

Creating a basic Mondrian OLAP Cube

simple mondrian

This post is a hands on tutorial on how to create an analysis cube for the Mondrian OLAP engine. It is an introductory post for an audience with no OLAP experience at all. I will assume some experience with relational databases and the Kettle ETL tool. If you’d like to follow the examples you will need access to a database, a copy of Pentaho Kettle and a Mondrian installation. I will be using MySQL as RDBMS and JasperServer 3.7. CE for the Mondrian installation. Other possibilities include Pentaho BI-Server and a bare bones Mondrian/JPivot and PAT installation.

The example data

For the example data I would like to use a small and simple dataset, so it is easy to share and easy to understand. Therefore I decided to use an Excel extract from the public issue tracker for the Kettle tool. It has one thousand lines of issues, bugs and feature requests that I would like to put into an OLAP cube for analysis. Each row contains the issue type, a summary, the assignee, a priority,  a status and a resolution. I took the liberty to replace the real names of the assignees with figures from Sesame Street in the input data. And no, I will not reveal who is who :-) . . . → Read More: Creating a basic Mondrian OLAP Cube