Matching entries using the Q-grams and Levenshtein algorithms - 7.0

Data matching

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Data Management Platform
Talend Data Services Platform
Talend MDM Platform
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Data Quality components > Matching components > Data matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Data matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Data matching components
EnrichPlatform
Talend Studio

This scenario applies only to Talend Data Management Platform, Talend Big Data Platform, Talend Real Time Big Data Platform, Talend Data Services Platform, Talend MDM Platform and Talend Data Fabric.

For more technologies supported by Talend, see Talend components.

This scenario describes a Job which uses a match rule based on the VSR algorithm. The Job aims at:

  • matching entries in the name column against the entries in the reference input file by dividing strings into letter blocks of length q, where q is 3, in order to create a number of q length grams. The matching result is given as the number of q-gram matches over possible q-grams,

  • checking the edit distance between the entries in the email column of an input file against those of the reference input file.

The outputs of these two matching types are written in three output files: the first for match values, the second for possible match values and the third for the values for which there are no matches in the lookup file.

In this scenario, we have already stored the main and reference input schemas in the Repository. For more information about storing schema metadata in the Repository, see Talend Studio User Guide.

The main input table contains seven columns: code, name, address, zipcode, city, email and col7. We want to carry the fuzzy match on two columns: name and email.