Designing a Transformation

Convert raw data to useful information


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 components for you to take a raw dataset, convert it to an organized structure, and provide the basis for a meaningful report.

When assembling a data transformation (such the above) you work with the Designer, which offers a palette of transformation components and many wizards to guide you through the process and speed up your work. Everything is automatically organized into a project, and all the pieces you pull together in order to achieve your goal are sorted into folders for easy navigation and manipulation.

A Selection of Frequent Data Operations
Reformatting—Change the structure or transform incoming data using standard operations (e.g. convert, trim, etc.) or custom functions. Deduplication—Remove duplicates from a data stream using various matching algorithms.
Aggregation—aggregate incoming data similarly to SQL GROUP BY command. Filtering—: Filter a data stream to "true" and "false" output ports based on user-defined conditions.
Sorting Options—sort data by selected fields in the data stream. Partitioning—Split your data stream into several output streams based on user-defined condition.
Joining & Concatenation—Join data based on a key or gather multiple streams into one. Merging—Take sorted input data from multiple ports and merge them based on a key.
Data Normalization—Break information from a single complex record into multiple output records of different formats. Rollup—Group incoming records by key values and create new recordsets by aggregating, composing, or splitting.
Data Replication—Copy data records from input to all connected outputs. Gather Data—Collect data from input ports and merge them into a single output.

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