Open source data quality tools: Revisited
Abstract
High data quality is defined as the reliability and application efficiency of data present in a system. Maintaining high data quality has become a key feature for most organizations. Different data quality tools are used for extracting, cleaning, and matching data sources. In this paper, we first introduce state of the art open source data quality tools, specifically Talend Open Studio, DataCleaner, WinPure, Data Preparator, Data Match, DataMartist, Pentaho Kettle, SQL Power Architect, SQL Power DQguru, and DQ Analyzer. Secondly, we compare these tools based on their key features and performance in data profiling, integration, and cleaning. Overall, DataCleaner scores highest among the considered tools.
This paper has been withdrawn.