Filter data early
If you know that you are dropping some deleting some rows and columns from your dataset, add these transformation steps early in your recipe. This reduction simplifies working with the content through the application and, at execution, speeds the processing of the remaining valid data. Since you may be executing your job multiple times before it is finalized, it should also speed your development process.
- To drop delete columns:
- Select Drop Delete from the column drop-down for individual columns. See Column Menus.
- Use the Delete Columns transformation to remove multiple discrete columns or ranges of columns.
To delete rows: The following example removes all rows that lack a value for the
D trans p03Value delete matching rows Type step p01Name Condition p01Value Is missing p02Name Column p02Value id p03Name Action SearchTerm Filter rows