Looking into the details of your dataset - Cloud

Talend Cloud Data Inventory Getting Started Guide

Version
Cloud
Language
English
Product
Talend Cloud
Module
Talend Data Inventory
Content
Administration and Monitoring > Managing connections
Data Governance
Data Quality and Preparation > Enriching data
Data Quality and Preparation > Identifying data
Last publication date
2024-03-21
The first window that opens after creating a dataset in Talend Cloud Data Inventory is the Overview tab of the dataset detailed view. This is also the page that you will reach by default when selecting a dataset from your list.

Aside from the capacity to store all your datasets, the application is also intended for you to be able to easily access metadata information and dig into the details and content of each of your assets. The entry point to this other degree of information is the dataset detailed view.

You can also reach the Sample and Configure tabs of your dataset, but for now, you will focus on the information available in the Overview tab.

The information that you can find at a glance is structured in the form of tiles:
  • Talend Trust Score™: Global quality indicator that aggregates several metrics into a single score, that scales from 0 to 5.
  • Details: Basic information about the dataset creator, the creation and last modification dates, as well as who modified it.
  • Data quality: Repartition of empty, invalid, and valid values in the dataset sample.
  • Data quality rules: Repartition of invalid, non-applicable, and valid values in each rule applied to the dataset.
  • Rating: Individual and global rating score of the dataset.
  • Description: Optional description that can be added to include any context information that you want to share on the dataset.
  • Pipelines: List of pipelines that use this dataset as source or destination.
  • Preparations: List of preparations that use this dataset as source, as well a list of preparations that are compatible with this dataset and can be directly applied.
  • Custom attributes: Apply metadata values to predefined attributes in order to better document and categorize your datasets.
  • Tags: Freely add labels to your dataset to describe its content and increase its searchability.