General
ETL software can achieve the data extraction, feature extraction, field cleaning and regularity, and dirty data filtering of different data sources, and finally load the cleaned data into the large data platform storage module, which lays the data foundation for the subsequent data analysis mining and data expression.
Function characteristics
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Data sources supported for acquisition include, text files (txt, csv, xml, json), relational data such as Oracle, Sql Server, Mysql, DB2, and so on, supporting JDBC, ODBC data driver to connect data sources.
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Support special data sources and application requirements. User can customize the plug-in.
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Support a variety of application systems, such as ERP, supply chain, financial systems, etc.
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Design, run, and debug Job (Job) and transform (Transformation) through graphical interface. Transformation completes the basic transformation of data, while Job is to complete the control of the entire workflow.
Technical Advantages
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Data from distributed, heterogeneous data sources can be extracted, cleaned, transformed, integrated, and finally loaded into a data warehouse or data mart to provide data application services.
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By integrating data, converting data into information and knowledge, it becomes the basis of report presentation, online analysis processing and data mining.