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Building a Scalable Data Warehouse with Data Vault 2.0 by Michael Olschimke, Dan Linstedt

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Chapter 13

Implementing Data Quality

Abstract

A key difference of the Data Vault model, as compared to other modeling techniques, is that it allows bad data into the Data Vault and applies business rules after loading the Data Vault. This chapter demonstrates how to deal with bad data in the Data Vault (for example, de-duplicating records with same-as links) and other examples. Another interesting topic is the application of Data Quality Services (DQS) to the Data Vault. DQS is a component of Microsoft SQL Server used for data cleansing. The authors discuss how to define domains in DQS, document them, and apply them to the data in the Data Vault.

Keywords

modeling techniques
bad data
data vault
data quality services
DQS
data cleansing
The goal ...

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