Creating a clean data set from the suspect pairs labeled by tMatchPredict and the unique rows computed by tMatchPairing - Cloud - 8.0

Data matching with Talend tools

Version
Cloud
8.0
Language
English
Product
Talend Big Data Platform
Talend Data Fabric
Talend Data Management Platform
Talend Data Services Platform
Talend MDM Platform
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Data matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Data matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Design and Development > Third-party systems > Data Quality components > Matching components > Continuous matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Data matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Last publication date
2024-02-06

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 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.