Collibra DQ User Guide

Collibra Data Quality & Observability

Autogenerate Rules, Anomaly Detection, Reconciliation
We've moved! To improve customer experience, the Collibra Data Quality User Guide has moved to the Collibra Documentation Center as part of the Collibra Data Quality 2022.11 release. To ensure a seamless transition, will remain accessible, but the DQ User Guide is now maintained exclusively in the Documentation Center.
Automatic Data Quality without the need for Rules. Collibra Data Quality provides a fast and elegant way to manage your data sets by learning through observation rather than human input. Collibra Data Quality applies the latest advancements in Data Science and Machine Learning to the problem of Data Quality. Surfacing data issues in minutes instead of months.

Getting Started with Collibra DQ

Deep Learning vs Machine Learning

What Does DQ Mean to You?

A Pluggable and Complete Data Quality Framework

If you are adding data quality to your data pipelines the below visual illustrates the number of products and pieces you will need consider to successfully complete your overall governance program. The Collibra Data Quality suite allows you to use either native CDQ components or integrate the 3rd party components of your choice. By using our best practice guide and framework you can complete the DQ lifecycle easily.

Data Pipelines that Tie into the DQ Framework

Collibra Data Quality offers a coding framework for developers or ETL designers that want to built real-time data quality into their broader data pipeline. This provides the same algos and DQ checks as the Collibra Data Quality UI Wizard but with direct access into your code points. Consistency is a must to have a program you trust.
Last modified 6mo ago