Scenario: Doing continuous matching using tMatchIndexPredict

Continuous matching

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Data Fabric
Talend Real-Time Big Data Platform
Talend Big Data Platform
task
Design and Development > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Continuous matching components
EnrichPlatform
Talend Data Stewardship
Talend Studio

This scenario applies only to a subscription-based Talend Platform solution with Big data or Talend Data Fabric.

For more technologies supported by Talend, see Talend components.

After indexing lookup data in Elasticsearch using tMatchIndex, you do not need to restart the matching process from scratch. The tMatchIndexPredict component compares new data records with the lookup stored in ElasticSearch.

In this example, a list of early childhood education centers in Chicago coming from ten different source has been cleaned, deduplicated and indexed in Elasticsearch. You want to match new records which contain information about early childhood education centers in Chicago against the reference data set stored in Elasticsearch.

tMatchIndexPredict uses pairing and matching models to group together records from the input data and the matching records from the reference data set indexed in Elasticsearch and label the suspect pairs.

tMatchIndexPredict outputs potential duplicates and unique records in separate files.

Before you begin: