Configure Universal Connector (UC)

SourceConnect Management Console provides comprehensive support for Universal Connector (UC), extending functionality beyond CSV file management. You can customize columns and mappings for UC directly within the Management Console interface.

Prerequisites

Before you create a UC instance, complete the following tasks:

Import a staging database template.
Import a UC source template.
Configure the data integration server.

To configure a UC instance

In the left navigation pane, select the second icon, and then select Data Sources. Alternatively, on the Templates page, select Actions > Create Data Source for an imported UC template.

In the Overview tab, enter values for the following options, and then select Save.

Option	Description
Name	Enter the name of the UC instance. The first 40 characters are used as a unique identifier.
Description	Enter a brief description of the instance.
Staging Area	Select the staging area.
Unique Identifier	The first 40 characters of the instance name are used as a unique identifier. You can modify this value.
Template Name	The template name is displayed.
Template Version	Select a version if applicable.

A success message appears.

Go to the Connection tab. From the Connection Type drop-down menu, select one of the following options:
- Flat File: Fetch data in CSV format.
- ODBC: Connect to a database.
- Stage: Create a table or view in the staging area.

Configure the selected connection type.

Flat File: Enter the appropriate details for the following options and select Save.

Option	Description
Download Sample	Download a sample flat file to view the format, including column types.
Flat File	Select a flat file to upload.
Source System	Enter the source system name and version. This enables AI to provide accurate assistance in the Mapping tab.
Skip Rows	Specify how many rows to skip at the beginning of CSV files before reading the actual column data. After data stores are deployed in the Data Pipeline tab, this value syncs with the `$G_UC_Skipped_Rows` global variable in the `SC Journals` job in Task Manager. Changes to either value will automatically update the other.
Folder Path	Select the folder where you want to store all CSV files.

In the Verify Source Columns dialog box, review the source columns in the uploaded CSV file, and then select Continue to confirm.

Note: When you re-upload a CSV file, you must choose to either reset or update the mappings in the confirmation prompt. If you reset the mappings, the Verify Source Columns dialog box shows the changes made to the column list.

ODBC: Select the DSN and object that contains your transaction data and then select Save to proceed.

Stage: Select the object that contains your transaction data and then select Save to proceed.

Go to the Mapping tab.
1. Select Map with Lineos to map source columns from the CSV file to target columns in the template using the insightsoftware AI service.
2. To map columns using the in-app ranking algorithm, select Auto Map and review the mappings when complete.
  Tip: For optimal performance, use Lineos for mapping instead of Auto Map.
3. Filter the results using the Document Type drop-down menu.
4. To manually map a source column to a target column, select the Edit icon next to the source expression. In the Source Expression Editor window, enter a SQL expression. For example, you can map the LINE_NUMBER source column to both the HEADER_ID and LINE_NUMBER target columns. You can also add custom SQL expressions and map them to target columns.
5. To update a blank target column, select the Refresh icon next to the respective source column, and then choose to map it using Lineos or auto-map.
6. To create and edit validation rules for mapped columns, select the Edit icon under Validation Expression for that column.
7. To mark a column as a unique record, select the Primary Key checkbox for that column. You can designate multiple columns as primary keys.
8. To perform a uniqueness check of columns, select Alternate Keys. In the Alternate Keys Configuration window, Enter a name and select key columns.
9. (Optional) To add more source columns, select Add Mapping.
10. Hover over the following icons for additional information:
  - Magic wand icon: Shows the justification for mapping against source columns.
  - Warning icons (source columns) - Indicates when column mapping has failed and requires attention.
  - Info icon (target columns): Displays mapped target column descriptions based on template information.
  - Magnifying glass icon: Shows a data preview of the top values in the target column.
  - Rounded alert icon – Alerts you when mapping was successful but the target column is already mapped to another source column or the target column is missing.
11. When you finish mapping columns, select Save.
Go to the Data Pipeline tab.
1. Select the data integration server that you configured.
2. Select the project that you want to deploy, enter the passphrase, and then select Deploy. The project deploys to the SAP Data Services repository. Select Save.
3. Select the Datastore Configuration button to configure datastores. Select the datastore that you want to deploy along with the cache datastore and corresponding connections, and then select Deploy. The datastores are deployed.
Select Task Manager in the left pane.
1. Run the following jobs for the SC_Journals project:
  - SC_Journals: Handles both initial (OT) and incremental (CDC) data loads for journal data, with an option to run L1-L3 reconciliation reports concurrently.
  - SC_Generate_Cache: Updates cache files, enabling them for transactions and syncback as applicable.
  - SC_Master_Lookup_Load: Loads master data from MSC_Lookup_Data.xlsx to the MSC_MASTER_LOOKUP table and creates a persistent cache on the MSC_MASTER_LOOKUP table for lookup data.
  - SC_Reprocess: Loads data for transactions that failed during initial and incremental data loads.
2. Correct any invalid data.
3. Run the load job. The UC transaction data processes successfully.

Configure Universal Connector (UC)

Prerequisites

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