How to Schedule Data Load

Aim of this article	Explains how to schedule a Data Load (a task/process for data manipulation).
Related sections	Data Loads (Reference Manual)

Steps:

Go to Analytics > Data Loads.
Select a Data Load.
In the Schedule section on the right, enter a Start Date.
Enter a Period (the load will run every X minute, hour or day).
Enter an Interval (e.g. if you set Period = day and Interval = 1 and Start Date = 27/11/2021 10:00, the Data Load will run every day at 10:00 am, starting on 27/11/2021).
If the Incremental option is checked, only new/changed data is loaded.
Incremental Load Date indicates the last time this task ran successfully. Only rows loaded/updated since this time are considered in the Data Load. Incremental Load Date can be edited to force in data older than the last successful run date.
For Calculation Data Load type, you can also enable the option With target snapshot, meaning that the target rowset will be pre-populated with the target rows in the scope of the Data Load (determined by the DL.filter). Otherwise this rowset is empty.
Use case for 'With target snapshot'
The option With target snapshot can be used in the following advanced use case. When a Calculation job is started, there are two DatamartRowSets available in the formula context: source and target (api.getDatamartRowSet("source")...). The target rowset is initially empty, unless With target snapshot was checked. The typical use case would be a Flush, where the data to be loaded in the Data Source depends on which data is already there, with Groovy code using the DatamartRowSet API to find and inspect existing rows etc.
Rowset holds the rows to be loaded in the target Field Collection. Initially this rowset is empty.
Target and Source are automatically generated. You need to specify these only if you set up the Data Load manually.

When you run a Data Load and get a "Connection refused by peer" message, it can refer to a data type issue of the given target field.