Getting Started
Deployment
Snowflake
Implementation
Primary Sources
Tables
Table sources are used directly via a view. Featureform will never write to a primary source.
Files
Files are copied into a Snowflake table via a Kubernetes Job kicked off by our coordinator. If scheduling is set, the table will atomically be re-copied over.
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 Snowflake configuration in Python.
snowflake_config.py
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import featureform as ff
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ff.register_snowflake(
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name = "snowflake_docs",
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description = "Example inference store",
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team = "Featureform",
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username = snowflake_username,
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password: snowflake_password,
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account: snowflake_account,
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organization: snowflake_org,
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database: snowflake_database,
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schema: snowflake_schema,
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)
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Once our config file is complete, we can apply it to our Featureform deployment
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featureform apply snowflake_config.py --host $FEATUREFORM_HOST
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We can re-verify that the provider is created by checking the Providers tab of the Feature Registry.
Last modified 23d ago