Impact analysis of the schema changes on distributed data warehouse systems

As the use of data warehouse is becoming more and more important in almost all organizations around the world, business requirements demand analytical information at anytime and anywhere. In order to address these business users' requirements, it is useful to keep data warehouse information stored in the user workstation, thus allowing local information analysis. In general, users' workstations have limited resources and users just need specific subsets of the data warehouse. This paper introduces a framework, describing the data structures and the processes needed to implement data marts at mobile platforms (laptops) synchronized with a central data warehouse, considering different users characteristics and needs. One critical issue of the framework must be carefully examined: the replication of schema evolution between the central data warehouse and local data marts at mobile platforms. As business requirements demand new functionalities, data warehouse schema must evolve and be updated and consequently local data marts schema need to reflect all these changes. Therefore, this paper also introduces a classification of schema changes usually found at data warehouse systems, evaluates the schema change complexity and estimates its impact on other data warehouse objects.