analytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In ordThere are two prevalent approaches to the development of Datawarehouse Architectures:

1. Data Warehouse (DWH) bus architecture (introduced by Ralph Kimball)

According to this approach the DWH is developed in phases. Each phase includes the development of a set of dimensional models which are linked together via conformed dimensions, thus forming a virtual ‘bus architecture’. Therefore, according to this approach, at the core of the DWH resides a denormalised dimensional data model, which handles data at the atomic level.The major advantages of this approach are inherited from the use of the dimensional model combined with the ‘conformed dimensions’ principle. This model’s simple and symmetric structure is easily understood by Business Analysts (easier than complex normalized data models). Moreover the so called ‘star schema’ allows the efficient execution of queries (less relational joins). The ‘conformed dimensions’ principle allows for the gradual development of a Data Warehouse, in which all information is linked efficiently and analytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In ordeWH is developed in phases. Each phase includes the development of a set of dimensional models which are linked together via conformed dimensions, thus forming a virtual ‘bus architecture’. Therefore, according to this approach, at the core of the DWH resides a denormalised dimensional data model, which handles data at the atomic level.The major advantages of this approach are inherited from the use of the dimensional model combined with the ‘conformed dimensions’ principle. This model’s simple and symmetric structure is easily understood by Business Analysts (easier than complex normalized data models). Moreover the so called ‘star schema’ allows the efficient execution of queries (less relational joins). The ‘conformed dimensions’ principle allows for the gradual development of a Data Warehouse, in which all information is linked efficiently and analytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In orddata model, which handles data at the atomic level.The major advantages of this approach are inherited from the use of the dimensional model combined with the ‘conformed dimensions’ principle. This model’s simple and symmetric structure is easily understood by Business Analysts (easier than complex normalized data models). Moreover the so called ‘star schema’ allows the efficient execution of queries (less relational joins). The ‘conformed dimensions’ principle allows for the gradual development of a Data Warehouse, in which all information is linked efficiently and analytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In ordthan complex normalized data models). Moreover the so called ‘star schema’ allows the efficient execution of queries (less relational joins). The ‘conformed dimensions’ principle allows for the gradual development of a Data Warehouse, in which all information is linked efficiently and analytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In ordanalytics spanning different business processes or subject areas are feasible. Each ‘star schema’ involves a fact table linked to a number of dimensions in a star.Three fundamental types of fact tables: transaction, periodic snapshot, and accumulating snapshot have been defined. In order to define a DWH development roadmap, Kimball introduced the concept of the DWH bus matrix. The ‘bible’ on this approach is: ‘ The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling’, John Wiley & Sons, 2002 Ralph Kimball and Margy Ross

1. Corporate Information Factory – CIF (introduced by Bill Inmon)

According to this approach, the first step involves the design of a comprehensive abstract data model for the Enterprise (a model mapping the way the Enterprise exploits information). Based on this abstract model, the central DWH data model is developed following a normalized design approach (3NF), which handles data at the atomic level.According to the Inmon approach, dimensional models embedding aggregated facts are built by querying this central atomic DWH data model and serve departmental needs (this is one of the major disagreements between the two thought leaders – read Kimball’s open letter to the data warehousing community).Both development approaches agree to the following points:

• Phased-iterative imple