Materializations cards

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Cards in this section support the analysis of materializations of index data from the Ontology.

Materialization is a data type in Quiver that provides a way to transform, visualize and analyze Ontology data at scale, with the capacity to surpass the 50k row constraint on data joins and transformations present in using transform tables.

Specifically, Materializations cards allow you to:

• Perform joins (left/right/inner/full) on objects data
• Derive new columns, filter, or aggregate objects data
• Plot data using categorical charts
• Convert to a transform table and use Vega plots

Use Materializations cards for large-scale analyses that take object sets as inputs, letting Quiver convert object sets seamlessly behind-the-scenes. From an object set, use any of the cards located under the new Materializations next actions, or perform an explicit conversion with the Object set materialization card.

To find out which backing dataset primitives are powering a Materializations card, simply hover over the datasource icon.

The following functions are available:

• Expression
• Filter materialization
• Join materializations
• Numeric aggregation (materialization)
• Object set materialization
• Set math (materialization)
• Unique column values (materialization)

Expression

Use the expression language to derive new columns or perform complex filtering. Advanced features available only in this card, such as window functions can be used to unlock new types of analysis in Quiver. To use, select either an object set, materialization card, or another Expression card.

Select Additional configuration... to access Add new column, Replace column, Filter, and Aggregate options.

Where AIP is enabled, you can select the AIP Configure option in the Expression card to create expressions using natural language.

Some sample prompts you can use include:

• “Compute a new column called 'total user score' by multiplying the two score columns”
• “Compute the 'total user score' defined by multiplying the two score columns for each organic category”
• “Compute the average sustainability score for each organic category”
• “Update the values in the Organic column to camel case”
• “Concatenate the two product name columns into one of them”
• “Compute the total revenue per product for each store, taking into account price and quantity sold”

AIP provides a suggestion which you can Apply.

Filter materialization

Apply additional filters to a materialization. Select an object set on which to apply conditions.

Join materializations

Performs a left, inner or right join of two materialized object data sets. Select which columns of data you wish to retain from the source and joining materializations. Optionally, add a prefix to incoming columns to avoid name collisions with existing columns, or to annotate the joined columns.

Numeric aggregation (materialization)

Compute an aggregation on a numerical materialization column. Select an object set as input, choose the appropriate metric and property from available columns.

Object set materialization

Use a materialization of the data from the underlying object set to perform flexible, high scale analysis.

When adding any materialization card from the next action menu of an object set card, Quiver will automatically add this card to convert the object set data type to a materialization data type and use it as the input to the added materialization card.

Set math (materialization)

Perform a union, intersection, or difference of two materializations.

Unique column values (materialization)

Retrieve all the unique values from a materialization column. Select an object set and a property to use.