Steps to use the Semantic-aware analysis - Cloud - 7.3

Talend Studio User Guide

Version
Cloud
7.3
Language
English
Product
Talend Big Data
Talend Big Data Platform
Talend Cloud
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Design and Development
Last publication date
2024-02-13
Available in...

Big Data Platform

Cloud API Services Platform

Cloud Big Data Platform

Cloud Data Fabric

Cloud Data Management Platform

Data Fabric

Data Management Platform

Data Services Platform

MDM Platform

Real-Time Big Data Platform

From Talend Studio, you can use the Semantic-aware analysis to:

  • Explore the semantic categories and query complex semantic relationships in the data you analyze,
  • Create table analyses preconfigured with indicators and patterns that best suit the data.
  • Index and enrich the ontology repository on the log server with semantic categories and analysis results.

The sequence of using the Semantic-aware approach to create pre-configured table analyses involves the following steps:

  1. Connecting to a data source from the Studio, whether it is a database, a delimited file or Hive.

    For further information, see Creating connections to data sources.

  2. Launching the log server where ontology indexes are stored.

    For further information, see Launching the server and setting preferences.

  3. Selecting a table in the data source or a view in a database connection and exploring semantic categories of data columns.

    You can also select to start a Semantic-aware analysis on a set of columns in a table.

    For further information, see Exploring semantic categories of data columns.

  4. Matching column metadata and semantic categories with the concepts from the Ontology repository and outputting the matching results to show the most relevant concepts.

    For further information, see Matching column metadata and semantic categories with the concepts in the ontology repository.

  5. Defining attributes (semantic) for columns and enriching the Ontology repository with column metadata and semantic categories.

    For further information, see Enriching the ontology repository.

  6. Running the recommended table analysis and enriching the Ontology repository with analysis results and indicators and patterns used on the analyzed columns.

    For further information, see Defining the recommended table analysis.