DataMart (Data Warehouse) Tool: Mondrian + JRubik

Edwin Rojas (CIP) ICIS workshop 2006, CIMMYT
May 2006

Data Warehouse Motivation and Examples
Data Warehouse Motivation Huge amounts of data need to be summarized in various forms to enable data creators and data users to get quick overviews and dig into details as needed with high performance and flexibility CIP Example Solutions

Data Warehouse Architectural
Data Warehouse Client (web, standalone app)

Data Warehouse Engine Data Warehouse Repository
(multidimensional data base)
Script
-Populate database for dimensional model -Regenerate aggregated tables

Data Source
(relational db, flat files)

Data Warehouse Types – Part I
In the OLAP world, there are mainly two different types: Multidimensional OLAP (MOLAP) and Relational OLAP (ROLAP). Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP and ROLAP. MOLAP, This is the more traditional way of OLAP analysis.

In MOLAP, data is stored in a multidimensional cube. The storage is not in the relational database,

but in proprietary formats. Advantages: Excellent performance: MOLAP cubes are built for fast data retrieval, and is optimal for slicing and dicing operations. Can perform complex calculations: All calculations have been pre-generated when the cube is created. Hence, complex calculations are not only doable, but they return quickly. Disadvantages: Limited in the amount of data it can handle: Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself. This is not to say that the data in the cube cannot be derived from a large amount of data. Indeed, this is possible. But in this case, only summary-level information will be included in the cube itself. Requires additional investment: Cube technology are often proprietary and do not already exist in the organization. Therefore, to adopt MOLAP technology, chances are additional investments in human and