Schema mapping is the process by which you can realign, combine, and rename individual data types in the transition from your data source, to the Adverity Platform, and beyond into your dashboards and visualizations. Schema can be used to great effect in analyzing data from disparate sources and formats.
Schema mapping is used to map source columns in extracts (a) to target columns in a Destination (b). This allows columns from different data sources, with different names but tracking the same elements, to be unified.
The schema further indicates whether a field should be treated as a metric (c) or a dimension (d). For most data sources, Adverity has system defaults loaded in to simplify the schema mapping process (e).
The column color indicates:
SETTING DEFAULT SCHEMA MAPPINGS
For the moment, we will advise you on a very simple schema for the single data source to which you have already connected.
To configure the Datastream created in the previous section, first ensure that you're in the Connect Element.
Navigate to the Datastream you created.
In the left-hand navigation, click on ‘Schema Mapping’.
The system will automatically analyze the source data to identify data field headings, and display a list of all those discovered.Note: This is where data fields can be combined or otherwise manipulated to display data as necessary, but for the moment we will simply apply default values. Adverity have created numerous default configurations, covering the typical categories and fields specific to each Datastream.
Click on ‘Restore Defaults’ to load in the pre-configured Schema.
You’ll see your data schema populate as per the below.
Note: You will see that some of your fields have been assigned a data category, either a dimension (a), or metric (b), as defined by the default schema. If any fields remain grey, this means that they have not been assigned a category and will not be exported to your Destination.
Click Save Mapping.
"What Did I Just Do?"
You have taken a set of data that had category titles relevant only within the original data source, and applied new default titles that can be unified with data from other sources to combine them into one single category. Now you can: