The key idea of CloverETL is that even very complex tasks can be described by a workflow. In CloverETL, we call these jobflows. Jobflows split into smaller operation units called transformation graphs, which are built using generic versatile components such as file reader, sorter, aggregator, etc.
In order to process a data set, multiple components are interconnected to form a data flow (a graph), which represents a data transformation job. For example, the task of reading the content of a Excel file, sorting it based on a certain column, filtering out all non US customers, and writing the result into database tables is called a CloverETL graph (a job) which consists of four components: an Excel reader, a Filter, a Sorter, and a component called DBOutputTable.
Transformation graphs can be combined into a jobflow defining the sequence in which the individual graphs are executed and, for example, what to do in case error occurs. We call this an orchestration.
Fundamental Aspects of CloverETL:
Have you ever received an Excel file full of raw data? Trying to dig through all of the fields and make sense out of them is not easy.
CloverETL provides a rich set of transformation tools for you to take a raw dataset, convert it to an organized structure, and provide the basis for a meaningful report.
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