AN IMPACT ANALYSIS OF EXTRACT TRANSFORM LOAD PROCESS FOR MAINTAINING THE SYSTEM OF DATA WAREHOUSE

Abstract

The Extract Transform Load (ETL) process involves extracting data from database sources, transforming them into a suitable form for research analysis, and then loading it into a data warehouse (DW) to support effective decision-support implementation. To maintain the target of the data warehouse, several issues are discussed in the DW life cycle, ETL processes, and impact analysis for maintaining the DW. This research focuses on the issue high frequency of data changes makes ETL processes difficult to propagate the data changes and to maintain the changes history in the DW life cycle. Therefore, the focus issues of this research are identifying factors for frequent data changes that occur in data sources and the DW structure that need to be modified by performing impact analysis. The general factors of data changes in DW were identified by questionnaire from 41 respondents, and the factor of impact analysis was evaluated using the statistic test method called Kruskal Wallis H Test to make a comparison between the impact analysis factor and the category of users. The aims of this research are to perform the impact analysis of the DW process in order to maintain the DW system and to achieve DW with the right requirements definition. In addition, this will help users working with the DW to understand the elements of impact analysis in DW, especially on how to ensure the DW process runs efficiently and successfully. Therefore, the database administrators, data analysts, and DW developers can utilize these research findings as a guideline to deal with the data changes in the DW process.