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  • Introduction
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    • Quickstart
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  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
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      • Scaling Feast
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    • Running Feast in production (e.g. on Kubernetes)
    • Upgrading for Feast 0.20+
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
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  • Reference
    • Codebase Structure
    • Type System
    • Data sources
      • Overview
      • File
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    • Offline stores
      • Overview
      • File
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    • Online stores
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    • Providers
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    • Batch Materialization Engines
      • Bytewax
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      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • [Alpha] AWS Lambda feature server
    • [Beta] Web UI
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    • [Alpha] Data quality monitoring
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
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    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  • Description
  • Examples
  • Supported Types

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  1. Reference
  2. Data sources

Snowflake

Description

Snowflake data sources are Snowflake tables or views. These can be specified either by a table reference or a SQL query.

Examples

Using a table reference:

from feast import SnowflakeSource

my_snowflake_source = SnowflakeSource(
    database="FEAST",
    schema="PUBLIC",
    table="FEATURE_TABLE",
)

Using a query:

from feast import SnowflakeSource

my_snowflake_source = SnowflakeSource(
    query="""
    SELECT
        timestamp_column AS "ts",
        "created",
        "f1",
        "f2"
    FROM
        `FEAST.PUBLIC.FEATURE_TABLE`
      """,
)

Supported Types

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Be careful about how Snowflake handles table and column name conventions. In particular, you can read more about quote identifiers .

The full set of configuration options is available .

Snowflake data sources support all eight primitive types, but currently do not support array types. For a comparison against other batch data sources, please see .

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