LogoLogo
v0.22-branch
v0.22-branch
  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Dataset
      • Entity
      • Feature view
      • Stream feature view
      • Feature retrieval
      • Point-in-time joins
      • Registry
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • Learning by example
    • Third party integrations
    • FAQ
  • Tutorials
    • Overview
    • Driver ranking
    • Fraud detection on GCP
    • Real-time credit scoring on AWS
    • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Using Scalable Registry
    • Building streaming features
  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
    • Running Feast in production
    • Deploying a Java feature server on Kubernetes
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
    • Adding or reusing tests
  • Reference
    • Codebase Structure
    • Data sources
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Offline stores
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Online stores
      • SQLite
      • Redis
      • Datastore
      • DynamoDB
      • PostgreSQL (contrib)
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • Go-based feature retrieval
    • [Alpha] Web UI
    • [Alpha] Data quality monitoring
    • [Alpha] On demand feature view
    • [Alpha] AWS Lambda feature server
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page
  • Description
  • Examples

Was this helpful?

Edit on GitHub
Export as PDF
  1. Reference
  2. Data sources

Snowflake

Description

Snowflake data sources allow for the retrieval of historical feature values from Snowflake for building training datasets as well as materializing features into an online store.

  • Either a table reference or a SQL query can be provided.

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`
      """,
)
PreviousFileNextBigQuery

Last updated 2 years ago

Was this helpful?

One thing to remember is how Snowflake handles table and column name conventions. You can read more about quote identifiers

Configuration options are available .

here
here