Implementation
Primary Sources
Tables
Table sources are used directly via a view. Featureform will never write to a primary source.Transformation Sources
SQL transformations are used to create a view. By default, those views are materialized and updated according to the schedule parameter. Deprecated transformations are converted to un-materialized views to save storage space.Offline to Inference Store Materialization
When a feature is registered, Featureform creates an internal transformation to get the newest value of every feature and its associated entity. A Kubernetes job is then kicked off to sync this up with the Inference store.Training Set Generation
Every registered feature and label is associated with a view table. That view contains three columns, the entity, value, and timestamp. When a training set is registered, it is created as a materialized view via a JOIN on the corresponding label and feature views.Configuration
First we have to add a declarative BigQuery configuration in Python.bigquery_config.py
Mutable Configuration Fields
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description
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credentials