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.
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.
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.
First we have to add a declarative BigQuery configuration in Python.
bigquery_config.py
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import featureform as ffbigquery = ff.register_bigquery( name="bigquery-quickstart", description="A BigQuery deployment we created for the Featureform quickstart", project_id="", dataset_id="", credentials_path="")
Once our config file is complete, we can apply it to our Featureform deployment