A Comparison of Microsoft Data Quality Services (Denali) vs. Melissa Data’s Data Quality Components for SSIS

Blog Administrator | Address Correction, Data Integration, Data Management, Data Quality | , , , ,

By Ira Whiteside

Recently Microsoft released a new Beta Release of SQL Server codenamed “Denali,” which included the Data Quality Services (DQS) feature. Here at Melissa Data, we are partners with Microsoft and also participate in the Azure Data Services Market for DQS providing Address Correction references.

Over the next several weeks, we intend to explore the new data quality services capabilities of the upcoming release of SQL Server codenamed Denali, Melissa Data provides additional components that accomplish data quality in the SQL Server and SQL Server Integration Services (SSIS) environment. I have outlined some of the differences below.

Data Quality Services is a service and server-based application relying on a data quality knowledge base, therefore providing a shared knowledge base or data-driven application, most probably available only in the Enterprise Edition

This will provide a powerful new capability, allowing for the code-free development of data quality capabilities that are accessible in an SSIS environment.

While this is a new set of capabilities provided by Microsoft, many of these capabilities are currently available to Melissa Data’s Data Quality Components for SSIS.

The Melissa Data DQC for SSIS are also data driven, share rules and domain knowledge and allow you to store knowledge in a similar fashion , however they differ in that they are implemented as SSIS Custom Components thereby fully leveraging the capabilities of SSIS pipeline capabilities and accessing local data stores.

Similarly, the Melissa Data reference libraries can be stored and accessed locally providing performance gains. They are available for all Editions of SQL Server.

In the next blog, we will take a deep dive into the SSIS implementations side-by-side and then move on to the impact on data governance and master data management efforts.