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Comment: Published by Scroll Versions from space DEV and version r081

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In the Join window of



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, you can join your current dataset with another dataset or recipe based upon information that is common to both datasets.

For example, you could join together two sets of regional sales data based upon the product identifiers that they both use to track sales. In the Search panel, enter join datasets or select the Join icon from the toolbar.


In the Search panel, enter join datasets.

Browse dataset and recipe list

Select Then, select the dataset or recipe that you wish to join with your current dataset.


  • Use the Search bar to locate specific objects.
  • Click Accept.
  • To browse objects in the current flow, click Browse current flow. See below.

Browse current flow

You can limit your review of the objects to browse to those in the current flow.

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Browse current flow


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Step 2 - Select Join Conditions


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Specify join type and join keys

Dataset samples:

Mouse over the Dataset samples indicator to see the current samples from the datasets that are part of the join. For more information, see Samples Panel.

Join type:

From the drop-down, select the type of join to apply. For more information, see Join Types.


fuzzy match

Use a fuzzy matching algorithm for key value matching.


Tip: Use this option to perform fuzzy join matching of primary keys between datasets.


NOTE: Fuzzy joins can only be applied to String data types. Other data types cannot be fuzzy-matched using the algorithm.

Fuzzy matching uses the doublemetaphone algorithm for matching strings (keys). Both primary encodings of each key value must match. See DOUBLEMETAPHONEEQUALS Function.

Ignore caseIgnore case differences between the join key values for matching purposes.
Ignore special charactersIgnore all characters that are not alphanumeric, accented Latin characters, or whitespace, prior to testing for a match.
Ignore whitespaceIgnore all whitespace characters, including spaces, tabs, carriage returns, and newlines.