Overview
Through Data modeling in Bizagi, we define and create entities, attributes and relationships directly in the local process' data model (at Bizagi's database).
When doing this, Bizagi will also offers a powerful integration mechanisms that allows you to directly integrate existing data sources into Bizagi's data model.
Data Replication is a data-level integration feature, presenting powerful benefits, such as the possibility to reuse and easily connect with external data models and their information.
The major concept is that Bizagi handles synchronization of such information and encapsulate this management, so that this is transparent for both Bizagi Studio users (e.g, when defining rules or the UI), or for the end users. This means that they will not need to know about the data source of such information, or do anything in special/additional when external data sources are integrated.
When to use it?
The mechanisms is especially useful in scenarios in which we want to integrate to Bizagi, any repositories in our corporate systems (e.g, using the information which is already in a CRM, ERP, legacy system, etc) and by leveraging reusable objects in Bizagi business process definitions.
|
Data Replication is supported using SQL Server and SQL Azure databases.
Data Virtualization is not recommended (mechanism available on-premises) |
Data Replication
Is used to get information from an external data source and into Bizagi (read-only), which can be programmed periodically as a background task.
Refer to Data Replication.
To view best practices oriented to the use of Data Virtualization and Data Replication, refer to Guidelines when integrating data.
Data Virtualization (deprecated)
This mechanism is applicable for customers who have moves from On-premises and have business cases reviewed and approved by Bizagi.
It works as a two-way communication that both gets information from, and updates information to, an external data source (Bizagi reads and writes), at runtime (on-demand).
Review Alternatives for Virtualization
Refer to Data Virtualization.
Last Updated 9/13/2023 9:07:23 AM