Talend Cloud Data Preparation - Cloud

Talend Cloud Release Notes

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Release Notes

Feature Description
Cross-column functions The introduction of functions applicable to multiple columns at once (such as concatenation and maths operations) brings improved efficiency for dataset cleansing and standardization.
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.

Convert character width You can now use this function to convert the character width to half or full width, and even normalize strings in your datasets.
Coalesce columns This function can be used to easily retrieve the first non null value across different columns to consolidate their data into a new column.

Known issues: https://jira.talendforge.org/issues/?filter=26475

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