This section explains how to create and configure your data cubes.
Create a new data cube from the home view by clicking the
+ button and selecting
New data cube from the menu.
Create new data cube modal allows you to define the source data for a data cube.
Auto fill dimensions and measures checkbox instructs Pivot to automatically detect your schema
and create dimensions and measures accordingly.
Another way to create a new data cube is to duplicate an existing data cube and edit its properties.
After creating the data cube you will be taken to the edit screen to tweak any fine details.
Click the pencil icon in the data cube header to edit a data cube.
Pivot will can automatically detect the columns in your data source and recommend some possible dimensions and measures.
Schema detection can be helpful when you are creating a new data cube in which case it will allow you to initialize the data cube with some dimensions and measures.
The other use for schema detection is when you evolve your schema and add columns over time.
If you have just added a column and you want a corresponding dimension to be created for it, you can use schema detection to automatically construct that column for you.
This functionality is accessible from the
Suggestions tab of the New dimension and New measure modals.
Pivot looks at the data source metadata and uses the returned list of columns, their types, and their aggregation (in case of rollup) to determine what dimensions and measures to suggest.
Pivot generates dimensions and measures by applying the following rules to the discovered underlying column types:
SUMmeasure or an otherwise appropriate measure if the column is marked as being aggregated as part of rollup.
While schema detection is an invaluable tool for quickly getting you up and running with a new data source, it can never do a perfect job.
Once you create a data cube you should play with it and taylor it to be perfectly suited to your needs -
don't hesitate to change and delete the auto-generated dimensions and measures - they will always be there in the
In particular, schema detection can not detect these common scenarios:
countDistinct- number of unique values.