The perfect Data Modeling Method that fits for all Data Warehouses…

…does not exist. Each Data Warehouse is different. Not only the known (or unknown) analytical requirements and the number, complexity and structure of the source systems have an impact on the architecture and design of a Data Warehouse.

Other aspects such as industry sector, company culture, used technologies and tools, data volumes and available knowledge in IT and business departments can have an influence on the recommended design methods.

In most Data Warehouses, star schemas or multidimensional cubes are used for the Data Marts. But how about modeling the Core Data Warehouse – the integration layer used in many DWH Architectures? What data models are appropriate for data integration and to store historical raw data in this Data Warehouse layer?

My two colleagues Maren Eschermann, Adriano Martino and me wrote the Trivadis white paper Comparison of Data Modeling Methods for a Core Data Warehouse. The purpose of this document is to give an overview of different modeling approaches and to compare their benefits and issues. The comparison and recommendations at the end of the document should help to decide what method is appropriate for the requirements of a particular Data Warehouse project.

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