A data warehouse is an IT system that offers mutual information from different internal and external sources to support business decision making.
The system contains roughly spoken of an area, where data from heterogeneous sources are loaded, aggregated and summarized and several departmental-individual data marts, where the processed data is loaded again and can be evaluated according to the different departments’ information and decision needs.
There are several approaches to building a data warehouse. The two most prominent ones are:
Ralph Kimball developed the bottom-up approach. Kimball starts by building the data marts, those being modelled in a Star Schema. Each data mart contains data on an atomic level as well as on a summarized level, representing all of todays and the future’s information needs. The different data marts are connected via a dimensional bus system, thus allowing the user to access all the data in all data marts. Hence, Kimball built a virtual data warehouse, where data marts are directly loaded with data from the operational systems through a staging area, where data is extracted, transformed and loaded (ETL).
Especially Bill Imnom as the proponent of the opposing architecture, the top down approach, attests the bottom up approach several disadvantages.4) He postulates a missing integration and doubts that redundancy can be avoided. In his view, data marts are developed totally autonomously from each other and thus may contain redundant data. Separate data marts containing different data may obstruct a company-unified view. An integrated sight is not possible. This is why each data mart needs to be constructed based on standardized dimensions and fact models. This approach enhances the complexity of constructing an integrated data warehouse and increases the danger of departments building stand-alone solutions. Furthermore, a data warehouse following the bottom up approach does not support batch processes. In order to conduct operational reporting processes, additional structures need to be implemented.
Building a data warehouse using the bottom up methodology provides for quick and cost efficient usage of data. This approach is suitable in less decentralized company structures, as stand-alone solutions and thus data redundancy should be avoided. The bottom up approach requires the strict and disciplined following of standardized dimensions in order to enable an integrated view on company's KPI's. When following these requirements, a user-friendly, flexible and early-to-use data warehouse can be built.
— Michaela Kaiser 2011/05/05 16:35