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After reviewing these questions, if you were unable to find a solution and/or are still experiencing issues, please contact us at We are here to assist.

How do I ignore or remove rows from a data file?

There are a number of ways to ignore or remove rows. OneCloud recommends a combination of Mapping Groups and the Smart Filter Pipeline transformation. First, chose a keyword such as Ignore to indicate field values that should be removed from the dataset. As necessary, utilize this keyword in Mapping Groups as a To value. Finally, configure the Smart Filter transformation to Remove matching rows when any mapped field equals the keyword (e.g., Ignore). Be sure to use an OR condition in the Rule section of the Conditions of the Smart Filter.

Ignore Smart Filter

Why did the text I entered into a transformation or mapping group condition disappear?

Any transformation or Mapping Group conditional that utilizes a comparison operator such as contains, greater than, equals (=), or does not equal (!=), the value to be evaluated must be input, and then the Enter key must be pressed to commit the value to the system. When the Enter key is pressed, the value entered will display in a gray bubble.

Valid form entry
Invalid form entry

⚠️ Be sure to review the field tips below the field to confirm if pressing the Enter key is required after input.

Can I see the before and after values at the same time for a field transformed by a Mapping Group?

When a Mapping Group is applied to a column in a Pipeline, the contents of the column are transformed inline. While you can navigate to an earlier transformation in the Pipeline and see the values before the Mapping Group was applied, you cannot by default see the unmapped and mapped values at the same time.

There is a very simple solution that we refer to as Data Lineage to address this need.

Prior to the Mapping Group transformation in the Pipeline, add a Copy Column transformation that makes a copy of the column to which the Mapping Group will be applied. The name of the copied column can be the same as the column from which it is copied but prefixed with an indicator that notates that the column is intended to create data lineage. Example prefixes are Src_, UM_, or DL_ which mean Source, Unmapped, and Data Lineage, respectively. The convention used does not matter as long as it is clear that the column represents the column values before the Mapping Group transformation.

Data Lineage

📓 Data lineage columns can be removed from the Pipeline output by using the Group By or Remove Column(s) transformations.

Can I do two-pass mapping in Data Prep?

Absolutely! Any column processed in a Pipeline can have multiple transformations applied to it including multiple Mapping Groups. Be sure to apply the Mapping Group transformations in the Pipeline in the proper order to account for the multi-staged mapping that will be performed.

Does my data file have to have a header?

Since Data Prep functions based on the header definitions and knowing where the data columns align; a header row in your data file is required. If your data is generated from a source that did not provide one, you can use the Tabular Transformation: Add Header command to insert one into your file before Data Prep processing occurs.

Why does my formula that works in Excel produce a "General Error"?

In the OneCloud version of Excel formula, the '=' operator is not needed before the start of building the formula. It is an assumption that is made on behalf of the platform.

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