Data Preparation: new features - 7.2

Talend Data Fabric Release Notes

author
Talend Documentation Team
EnrichVersion
7.2
EnrichProdName
Talend Data Fabric
task
Installation and Upgrade
Feature Description
Magic Fill This new function allows you to define a pattern based on a handful of examples, and via a machine learning algorithm, apply the transformation on a whole column. The Magic Fill gives you many formatting possibilities, on any data type.
Extract part of a name It is now possible, by leveraging a machine-learning model, to split a full name into its respective subparts such as title, first name, middle name, last name, and suffix, thus increasing efficiency for dataset cleansing and standardization.
Extract parts of a field based on semantic definitions It is now possible, leveraging the definition of semantic types, to extract various types of information contained in a single cell, into individual columns, thus increasing efficiency for dataset cleansing and standardization.
Repeatable masking and compound semantic types masking Data masking has been improved and can now handle seeds, to offer repeatable masking. Which means that identical source values will always be output as the same masked values.

In addition, semantic masking can now be performed on compound semantic types, enhancing data privacy.

Auto-completion Editing a cell from a column which semantic type is based on a dictionary is now easier than before, with the addition of auto-completion. Choose from a list of suggested values to guarantee that your data follows the standard of your semantic types.
Deduplication In addition to the existing deduplication function that can be applied on the whole table, you can now apply a deduplication operation based on the values of one or more columns, giving you more control on which rows you want to delete.