A connector handles the process of extracting data from a source system and transforming the data to make it usable for UiPath Process Mining. In a SQL connector, Extract and Transform are separate steps, of which the results are stored in the same database. Process Mining loads data from that database as input for the dashboards. See the illustration below.
A released app contains the dashboards definitions and covers all the steps to display the data into the dashboards.
In the Extract step, the data is extracted from the source system and stored in a database, without making any changes to the data (only filtering is applied to limit data necessary for the connector). The preferred extraction tool is CData Sync. Depending on the connector and source system, other tools may be used.
Transformations are applied to the data stored in the database to make sure the data adheres to a data schema which can be loaded in Process Mining.
In the Transform step, dbt is used to manage and run all the transformations, which are defined as queries in the Structured Query Language (SQL). Dbt is required for a SQL connector.
A SQL connector requires a Microsoft SQL Server database. This database server is not provided as part of the UiPath Process Mining product.
Refer to the Discover section in the Product lifecycle page for an overview of all supported apps, discovery accelerators, and connectors available for Process Mining.
If there is no connector available for your process, you can use the template connector in the devkit-connector that contains the basic setup for a SQL connector.
Next to Microsoft SQL Server and dbt, when you want to develop a SQL connector, a code editor is necessary.
It is strongly advised to also use a version control system.
Editing the transformations and configurations.
Visual Studio Code, which integrates with other tools needed for SQL connector development.
Tracking changes and collaborating on the connector development.
Updated about a month ago