Creating and Updating Transforms

An Ascend Transform is created and connected to a Read Connectors , Data Feeds, or another Transform component. A Transform component performs operations like cleaning, filtering, joining, and/or aggregating across data sets. These operations are written in SQL, PySpark, and Scala / Java .

Metadata Columns

Transforms have access to the metadata of their inputs, exposed through metadata columns.

For SQL Transforms, the metadata column name is referenced directly in the SQL statement -- it will be automatically detected and included.

For PySpark Transforms, as well as Scala / Java Transforms, the user-supplied function must implement an additional method to request the specified metadata column in the inputs list. Please refer to the PySpark Transforms or Scala & Java Transforms documentation pages for the interfaces.

Supported Metadata Columns:

Column Name



The full original filename of the input partition. This column must be used directly downstream of a Read Connector and requires the transformation to be a 'mapping' transform (see partitioning strategies for more detail). In all other cases, an attempt to use this column will error.

Updated 11 days ago


Creating and Updating Transforms

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