Schemas are a vital part of the creation of dataflows, as they translate your data to fit it to the structure that Raptor system expects. They can be altered and crafted during the dataflow creation process, and the list of your available schemas is available via the 'Schemas' button in the top-bar or the 'Edit Schemas' button on the Welcome-screen.
Likewise, Sources play an important role in categorizing and directing your dataflows, and can be viewed via the 'Sources' button in the top-bar or the 'Edit Sources' button on the Welcome-screen.
You can easily navigate between these two views and the Dataflow overview at any time using the top-bar.
The Schema Overview provides a number of useful features. A search-bar at the top lets you filter the list as needed, while the action-button at the right-hand side of each line lets you delete schemas that are not in use, or clone them to a sister account.
In addition to the name of each schema, the overview provides a variety of useful information.
- ‘Used in Dataflow’ will show whether the schema is currently in use, and if so, where. If the schema is used for two or more dataflows, the name of the first to use it is shown along with ‘+ others’, with a full list being displayed if this word is moused over
'Platform' and 'Parent Schema' are selected during Schema Creation, and will be discussed below
‘Modified by’ shows who created or last edited the schema. ‘System’ denotes that it is one of the default schemas included by default.
1.1 Create a Schema
Hitting the ‘Create new schema’ button lets you make a new schema from scratch. This will take you to the Schema Creation screen.
Depending on the schema-type selected, there may be one or more default-columns built into it – these cannot be edited or removed, and are necessary in order for the destination platform to use the data. A specific type of column may also be mandatory, such as a Person ID type column for a Customer Interaction type schema – for these, you will have a number of different options to choose from, but adding at least one of them to your schema is necessary before you can start adding custom columns.
Non-Customizable Schema for RecommendationEngine
If you select RecommendationEngine as the destination of a schema, you get two options - neither of which can be customized. RecommendationEngine requires some fairly specific inputs in order to function correctly, after all. Product Catalog is fairly straightforward, but the other option - Order Line - has some unique complexities. For more on this, see OrderLine Schema: Custom-Made for Point-Of-Sale Data
1.2 Add Custom Columns
Once any mandatory columns have been added, custom columns can be added using the ‘+ Add custom column’ button.
Here, you simply type in a name for the new column, and select what type of data it should contain. In the example above, we have added Cookie ID as a GUID. Optionally, the checkbox can be used to mark the new column as Required, making it mandatory when columns are mapped onto this schema during creation of the dataflow.
Custom columns can be edited or removed at any time, though required columns – such as the Person ID column needed for an Interaction-type schema – can only be deleted, and if so will need to be replaced by a different column that fulfills the requirements before the schema can be saved.
1.3 Edit a schema
Selecting a schema from the overview list allows you to use the creation-tool to edit it, albeit with certain limitations. The Destination Platform and Schema Type selection is locked in upon creation, and cannot be altered at any point afterwards.
The same applies even if the schema has been used in a 'Draft'-status dataflow - that is, you may still edit everything short of Destination Platform and Schema Type at this point. However, if the schema has been used in a published dataflow, edit-options are somewhat curtailed.
Under such circumstances, it will - naturally - not be possible to make any changes to existing columns. Nor can you add any additional Person ID columns or Required Columns, since this would cause conflict with existing uses with it. However, you can still change the schema's name, and add additional, optional, Custom Columns for use in future applications of the now-expanded schema.
The main role of Sources is as a tool for categorizing where the data comes from. A Source, in essence, is simply a name that is used to categorize the attached dataflow for searchability and easy access in a module such as the Customer Data Platform’s Audience Builder.
Creating a new Source is as simple as typing the desired name into the ‘Create new source’ field and clicking on ‘Save source’.
In the source overview, a search-feature is provided for filtering, and the overview shows name as well as who created the Source – and when. The ‘Used on Dataflow’ column, like in the Schema Overview, shows only the first to use it along with a ‘+ other’ line that can be expanded to a full list on mouseover.
Whether in use or not, a Source can be edited at any time - though of course, the only thing there is to edit is its name...