Scenario 1: Matching entries using the Q-grams and Levenshtein algorithms
This scenario applies only to a subscription-based Talend Platform solution or Talend Data Fabric.
This scenario describes a Job which uses a match rule based on the VSR algorithm. The Job
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,
zipcode, city, email
and col7. We want to carry the fuzzy match on two columns:
name and email.