Improving the Data Warehouse Architecture Using Design Patterns

Abstract

Data warehousing is an important part of the enterprise information system. Business intelligence (BI) relies on data warehouses to improve business performance. Data quality plays a key role in BI. Source data is extracted, transformed, and loaded (ETL) into the data warehouses periodically. The ETL operations have the most crucial impact on the data quality of the data warehouse. ETL-related data warehouse architectures including structure-oriented layer architectures and enterprise-view data mart architecture were studied in the literature. Existing architectures have the layer and data mart components but do not make use of design patterns; thus, those approaches are inefficient and pose potential problems. This paper relays how to use design patterns to improve data warehouse architectures