The semantics API enables your organisation to service downstream data applications with custom queries. The semantic model defines all entities, measures, metrics, timestamps and its relations. The data itself has been cleaned and transformed by codified SQL transformations in the datawarehouse using dbt.
Cube generates custom queries based on a dedicated semantic model and the incoming API requests are routed as Standard-SQL statments to a BigQuery datawarehouse. Commonly recurring queries are auto-cashed based on the volume of usage and some are explicitly pre-cached based on frequently used filter-settings. This setup enables cube to respond in miliseconds on queries that without the semantic caching layer would take several seconds.
The below interactive UI is connected to a demonstration semantics API built with cube. The API is connected to a BigQuery dataset with 28 million records representing all the invoices sent by the Alcoholic Beverage Division from the State of Iowa since 2012. The configuration of the semantic model can be found in our Github. To get a better feel for the underlying data behind the semantics API, we refer you to the dedicated Internal Analytics visualisations using Looker Studio.