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  • Introduction
  • Quickstart
  • Getting started
    • 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
  • Community
  • Roadmap
  • Changelog
  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
      • Google Cloud Platform
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
        • Azure AKS (with Terraform)
        • Google Cloud GKE (with Terraform)
        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
        • Feast CLI
      • Learn Feast
    • Concepts
      • Overview
      • Architecture
      • Entities
      • Sources
      • Feature Tables
      • Stores
    • Tutorials
      • Minimal Ride Hailing Example
    • User guide
      • Overview
      • Getting online features
      • Getting training features
      • Define and ingest features
      • Extending Feast
    • Reference
      • Configuration Reference
      • Feast and Spark
      • Metrics Reference
      • Limitations
      • API Reference
        • Go SDK
        • Java SDK
        • Core gRPC API
        • Python SDK
        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
      • Audit Logging
      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
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  • Example
  • Permissions

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  1. Reference
  2. Providers

Google Cloud Platform

Description

  • Offline Store: Uses the BigQuery offline store by default. Also supports File as the offline store.

  • Online Store: Uses the Datastore online store by default. Also supports Sqlite as an online store.

Example

feature_store.yaml
project: my_feature_repo
registry: gs://my-bucket/data/registry.db
provider: gcp

Permissions

Command

Component

Permissions

Recommended Role

Apply

BigQuery (source)

bigquery.jobs.create

bigquery.readsessions.create

bigquery.readsessions.getData

roles/bigquery.user

Apply

Datastore (destination)

datastore.entities.allocateIds

datastore.entities.create

datastore.entities.delete

datastore.entities.get

datastore.entities.list

datastore.entities.update

roles/datastore.owner

Materialize

BigQuery (source)

bigquery.jobs.create

roles/bigquery.user

Materialize

Datastore (destination)

datastore.entities.allocateIds

datastore.entities.create

datastore.entities.delete

datastore.entities.get

datastore.entities.list

datastore.entities.update

datastore.databases.get

roles/datastore.owner

Get Online Features

Datastore

datastore.entities.get

roles/datastore.user

Get Historical Features

BigQuery (source)

bigquery.datasets.get

bigquery.tables.get

bigquery.tables.create

bigquery.tables.updateData

bigquery.tables.update

bigquery.tables.delete

bigquery.tables.getData

roles/bigquery.dataEditor

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Last updated 3 years ago

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