LogoLogo
v0.23-branch
masterv0.11-branchv0.12-branchv0.13-branchv0.14-branchv0.15-branchv0.16-branchv0.17-branchv0.18-branchv0.19-branchv0.20-branchv0.21-branchv0.22-branchv0.23-branchv0.24-branchv0.25-branchv0.26-branchv0.27-branchv0.28-branchv0.29-branchv0.30-branchv0.31-branchv0.34-branchv0.35-branchv0.36-branchv0.37-branchv0.38-branchv0.39-branchv0.40-branchv0.41-branchv0.42-branchv0.43-branchv0.45-branchv0.45-branchv0.46-branchv0.47-branchv0.48-branch
v0.23-branch
masterv0.11-branchv0.12-branchv0.13-branchv0.14-branchv0.15-branchv0.16-branchv0.17-branchv0.18-branchv0.19-branchv0.20-branchv0.21-branchv0.22-branchv0.23-branchv0.24-branchv0.25-branchv0.26-branchv0.27-branchv0.28-branchv0.29-branchv0.30-branchv0.31-branchv0.34-branchv0.35-branchv0.36-branchv0.37-branchv0.38-branchv0.39-branchv0.40-branchv0.41-branchv0.42-branchv0.43-branchv0.45-branchv0.45-branchv0.46-branchv0.47-branchv0.48-branch
  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • Registry
      • [Alpha] Saved dataset
    • Architecture
      • Overview
      • Registry
      • Offline store
      • Online store
      • Batch Materialization Engine
      • Provider
    • Learning by example
    • Third party integrations
    • FAQ
  • Tutorials
    • Sample use-case tutorials
      • 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
    • Upgrading for Feast 0.20+
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
      • Adding a new online store
      • Adding a custom provider
    • Adding or reusing tests
  • Reference
    • Codebase Structure
    • Data sources
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
    • Offline stores
      • Overview
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
    • Online stores
      • SQLite
      • Snowflake
      • Redis
      • Datastore
      • DynamoDB
      • PostgreSQL (contrib)
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • [Alpha] AWS Lambda feature server
    • [Beta] Web UI
    • [Alpha] On demand feature view
    • [Alpha] Data quality monitoring
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page
  • Description
  • Example
  • Functionality Matrix
  1. Reference
  2. Offline stores

Snowflake

PreviousFileNextBigQuery

Last updated 2 years ago

Description

The offline store provides support for reading .

  • All joins happen within Snowflake.

  • Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Snowflake as a temporary table in order to complete join operations.

Example

feature_store.yaml
project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: snowflake.offline
  account: snowflake_deployment.us-east-1
  user: user_login
  password: user_password
  role: sysadmin
  warehouse: demo_wh
  database: FEAST

The full set of configuration options is available in .

Functionality Matrix

Snowflake

get_historical_features (point-in-time correct join)

yes

pull_latest_from_table_or_query (retrieve latest feature values)

yes

pull_all_from_table_or_query (retrieve a saved dataset)

yes

offline_write_batch (persist dataframes to offline store)

yes

write_logged_features (persist logged features to offline store)

yes

Below is a matrix indicating which functionality is supported by SnowflakeRetrievalJob.

Snowflake

export to dataframe

yes

export to arrow table

yes

export to arrow batches

no

export to SQL

no

export to data lake (S3, GCS, etc.)

yes

export to data warehouse

yes

export as Spark dataframe

no

local execution of Python-based on-demand transforms

yes

remote execution of Python-based on-demand transforms

no

persist results in the offline store

yes

preview the query plan before execution

yes

read partitioned data

yes

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Snowflake offline store.

To compare this set of functionality against other offline stores, please see the full .

Snowflake
SnowflakeSources
SnowflakeOfflineStoreConfig
here
functionality matrix