Creating a clean data set from the suspect pairs labeled by tMatchPredict and the unique rows computed by tMatchPairing

This scenario applies only to subscription-based Talend Platform products with Big Data and Talend Data Fabric.

In this example, there are two sources of input data:

The suspect records labeled as duplicates and grouped by tMatchPredict.

For an example of how to label suspect pairs with assigned labels, see Labeling suspect pairs with assigned labels.
The unique rows computed by tMatchPairing.

For examples of how to compute unique rows from source data, see Computing suspect pairs and suspect sample from source data and Computing suspect pairs and writing a sample in Talend Data Stewardship.

The use case described here uses two subJobs:

In the first subJob, tRuleSurvivorship processes the records labeled as duplicates and grouped by tMatchPredict, to create one single representation of each duplicates group.
In the second subJob, tUnite merges the survivors and the unique rows to create a clean and deduplicated data set to be used with the tMatchIndex component.

The output file contains clean and deduplicated data. You can index this reference data set in ElasticSearch using the tMatchIndex component.

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