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Master Data Management (MDM) on Azure – How to use Profisee with Azure Data Factory


An obvious – but all too frequently overlooked – aspect of any analytic project is that the data must be of sufficiently good quality that it can support the desired analysis. As it is often coming from multiple sources, the data in Azure can be particularly susceptible to this issue if not handled proactively.


Profisee MDM is a master data management platform, built on Azure, and designed to ‘combine and align’ data from multiple sources and deliver trusted, relevant, and authoritative information to drive the efficiency and effectiveness of Azure. Using the Profisee REST gateway, 3rd party services can connect with Profisee using REST API.


Azure Data Factory (ADF) templates for Profisee are now available to enable you to use Profisee with various Azure Data services. These ADF templates provide a reference architecture blueprint on how to read and write data using the Profisee’s REST Gateway API and ADF.


Architecture and Solution



Figure 1 – Azure Data Factory and Profisee Architecture Solution 

From Figure 1, Ingress and Egress modules enable Azure data integration for Profisee, using Azure Data Factory to enable data ingestion, and transformation. Figure 1 also shows the details for integrating with the Profisee Master Data Management solution.

We are excited to share the release of Profisee ARM Template. These templates show how you can use Azure Kubernetes Service  (AKS), Azure SQL Server, NGNIX server, and Azure Data Factory. The ARM template deploys Profisee server on Azure. 


Key to note is that Azure Data Factory and Profisee include native REST integration support, providing a lightweight and modern integration.

  • Load Source Data to MDM – Azure Data Factory is used to extract the data from the data lake, SaaS sources etc., transform it to align to the master data model, and load it into the MDM repository via a REST sink.

  • Master Data Management Processing – The MDM platform processes source master data through a sequence of activities to verify, standardize and enrich the data, as well as execute data quality processes. Finally, matching and survivorship is performed to find and group duplicate records and create master records. Optionally, data stewards can be issued tasks to perform data stewardship. The result is a set of high-quality, trusted master data for use in downstream analytics, machine learning and so on.

  •  Load Master Data for Analytics – Azure Data Factory uses its REST source to load master data from Profisee to Azure Synapse Analytics

Azure Data Factory Templates for Profisee


In collaboration with Microsoft, Profisee has developed a set of Azure Data Factory templates that make it faster and easier to integrate Profisee into the Azure Data Services ecosystem. These templates use Azure Data Factories REST data source and data sink to read and write data from Profisee’s REST Gateway API. Templates are provided for both reading from and writing to Profisee.



Figure 2 – Azure Data Factory pre-built templates for Profisee


ADF REST connectors enable Profisee to write  Profisee model objects into Profisee using the Profisee REST gateway. ADF enables Profisee to load data from data lakes, Dynamics 365, Salesforce and more. In addition, ADF enables customers to ingest Profisee objects, and enabling customers to perform ETL tasks on Profisee objects. Choices of integration are plenty given the ADF connector portfolio.


Click here for additional documentation on the Azure Data Factory templates for Profisee.



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