Derived data

Derived data lets you to reference results in new queries, query stacking, and eases cohort analysis. Cloudera Data Visualization uses derived data for computed fields in data modeling, weighted sums and averages, custom binning, for set-based and group-based analysis, and for combining data from different data sources.

Derived data enables you to reference query results in new queries, in essence "stacking" results from sub-select queries. The derived data feature also supports cohort analysis, where a set of data from a report is used (joined back) in another report, and allows you to build computed columns for re-use.

Derived Data is very useful in determining weighted averages and other, more complex calculations. For example, in the dataset World Life Expectancy, life expectancy is reported at the level of each country, for each year. If you wanted to determine the life expectancy by region or subregion, you have to calculate a weighted average of life expectancies. You can also parametrize derived data definitions using bracket notation.

The following steps demonstrate how to use derived data on a table visual based on the dataset World Life Expectancy [data source samples.world_life_expectancy].

1. Place the fields un_region, un_subregion and country on the Dimension shelf.
2. Place the field population, as sum(population), on the Measures shelf.
3. Place the field year on the Filters shelf, and change the expression to [year]=<<year_param:2000>>. This enables you to dynamically change derived data calculations. You must specify a default value in the parametrized expression.