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.
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Talend Open Studio. https://www.talend.com/resource/data-quality-tools.html
Data Cleaner. http://datacleaner.org/docs
WinPure clean & Match. http://www.winpure.com/cleanmatch.html
Data Preparator. http://www.datapreparator.com/
Data Match. http://dataladder.com/data-matching-software/
DataMartist. http://www.datamartist.com/
Pentaho Kettle. http://wiki.pentaho.com/display/EAI/Latest+Pentaho+Data+Integration+%28aka+Kettle%29+Documentation
Pentaho Kettle. https://help.pentaho.com/Documentation/5.4/0T0/040
SQL power architect. http://www.sqlpower.ca/page/architect
SQL Power DQguru. http://www.sqlpower.ca/page/dqguru
DQ Analyzer. https://www.ataccama.com/products/dq-analyzer
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© 2016 Springer International Publishing Switzerland
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Pulla, V.S.V., Varol, C., Al, M. (2016). Open Source Data Quality Tools: Revisited. In: Latifi, S. (eds) Information Technology: New Generations. Advances in Intelligent Systems and Computing, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-319-32467-8_77
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DOI: https://doi.org/10.1007/978-3-319-32467-8_77
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