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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
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        • Core gRPC API
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        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
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      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
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  1. Concepts

Offline store

PreviousOnline storeNextProvider

Last updated 3 years ago

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Feast uses offline stores as storage and compute systems. Offline stores store historic time-series feature values. Feast does not generate these features, but instead uses the offline store as the interface for querying existing features in your organization.

Offline stores are used primarily for two reasons

  1. Building training datasets from time-series features.

  2. Materializing (loading) features from the offline store into an online store in order to serve those features at low latency for prediction.

Offline stores are configured through the . When building training datasets or materializing features into an online store, Feast will use the configured offline store along with the data sources you have defined as part of feature views to execute the necessary data operations.

It is not possible to query all data sources from all offline stores, and only a single offline store can be used at a time. For example, it is not possible to query a BigQuery table from a File offline store, nor is it possible for a BigQuery offline store to query files from your local file system.

Please see the reference for more details on configuring offline stores.

feature_store.yaml
Offline Stores