Concepts of CloverETL

From components to jobflows

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.

From the Bottom Up—Components to Graphs

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.

Orchestrating Graphs

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:

  • Java based—Where Java runs, Clover runs too. Supported platforms include Windows, Unix, Linux, OSX, and many others
  • Visual design—Data transformations are designed visually in the CloverETL Designer. It's much quicker and less error prone than traditional scripting/coding
  • XML-based resources—All resources, such as graphs, metadata, shared connections, etc., are stored in an XML format. Thus, transformations may also be generated (by anything able to create a XML file)
  • Engine based—You deploy a data transformation engine that executes your transformation prescriptions, known as CloverETL graphs. With the Clover engine being constantly improved, future runs of your transformations will benefit from the improvements
  • Performance—Highly optimized to cope with huge data volumes; can utilize multiple CPUs/cores and also run on a cluster of computers to maximize performance
  • More than traditional batch-oriented ETL—Real time capabilities allow usage of CloverETL in more transaction oriented setups (e.g. web-services/SOA/ESB)


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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