To review the schema mapping go to the Datastream’s dashboard and click Schema Mapping.
The schema mapping is used to map source columns in extracts (1) to target columns in destination (2). Thus columns from different datasources and different names, but same meaning are unified.
The schema further indicates whether a field should be treated as a metric (3) or a dimension (4). For most datasources, Adverity has system defaults loaded in to simplify the schema mapping process (5).
The color of the column indicates
- grey: not mapped
- green: target column is a metric (3)
- blue: target column is a dimension (4)
If a new target schema field is needed, this can be added by users directly in the Data Schema menu.
In order to achieve a harmonisation in the destination, fields that contain the same meaning across different datasources,should be mapped to the same target column.
- Google Ads: Campaign -> campaign
- Facebook Ads: campaign_name -> campaign
- Google Analytics: ga:campaign -> campaign
- Other source: CAMPAIGN -> campaign
- The schema mapping will only populate once you have fetched at least 1 extract with 1 row of data so the column names can be extracted. If your schema mapping is empty, please perform a successful fetch first.
- Dimensions can be defined as Key Column to uniquely identify datasets when the target destination is Adverity Insights or a SQL database.
- Each target column can be used only once per datastream
- To import to Adverity Insights map at least 1 dimension, 1 metric and 1 date (to target column day)
- It is possible to define Schema Mapping Defaults per datasource