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  • Introduction
  • Community & getting help
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Architecture
      • Overview
      • Language
      • Push vs Pull Model
      • Write Patterns
      • Feature Transformation
      • Feature Serving and Model Inference
      • Role-Based Access Control (RBAC)
    • Concepts
      • Overview
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • Permission
      • [Alpha] Saved dataset
    • Components
      • Overview
      • Registry
      • Offline store
      • Online store
      • Batch Materialization Engine
      • Provider
      • Authorization Manager
    • 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
    • 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
      • Scaling Feast
      • Structuring Feature Repos
    • Running Feast in production (e.g. on Kubernetes)
    • 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
    • Type System
    • Data sources
      • Overview
      • File
      • Snowflake
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      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
    • Offline stores
      • Overview
      • Dask
      • Snowflake
      • BigQuery
      • Redshift
      • DuckDB
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Remote Offline
    • Online stores
      • Overview
      • SQLite
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      • Remote
      • PostgreSQL (contrib)
      • Cassandra + Astra DB (contrib)
      • MySQL (contrib)
      • Hazelcast (contrib)
      • ScyllaDB (contrib)
      • SingleStore (contrib)
    • Registries
      • Local
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      • SQL
      • Snowflake
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
      • Azure
    • Batch Materialization Engines
      • Snowflake
      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • Offline Feature Server
    • [Beta] Web UI
    • [Beta] On demand feature view
    • [Alpha] Vector Database
    • [Alpha] Data quality monitoring
    • [Alpha] Streaming feature computation with Denormalized
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  • Feast project structure
  • Data ingestion
  • Feature registration and retrieval

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  1. Getting started
  2. Concepts

Overview

PreviousConceptsNextData ingestion

Last updated 6 months ago

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Feast project structure

The top-level namespace within Feast is a project. Users define one or more within a project. Each feature view contains one or more . These features typically relate to one or more . A feature view must always have a , which in turn is used during the generation of training and when materializing feature values into the online store. You can read more about Feast projects in the .

Data ingestion

For offline use cases that only rely on batch data, Feast does not need to ingest data and can query your existing data (leveraging a compute engine, whether it be a data warehouse or (experimental) Spark / Trino). Feast can help manage pushing streaming features to a batch source to make features available for training.

For online use cases, Feast supports ingesting features from batch sources to make them available online (through a process called materialization), and pushing streaming features to make them available both offline / online. We explore this more in the next concept page ()

Feature registration and retrieval

Features are registered as code in a version controlled repository, and tie to data sources + model versions via the concepts of entities, feature views, and feature services. We explore these concepts more in the upcoming concept pages. These features are then stored in a registry, which can be accessed across users and services. The features can then be retrieved via SDK API methods or via a deployed feature server which exposes endpoints to query for online features (to power real time models).

Feast supports several patterns of feature retrieval.

Use case
Example
API

Training data generation

Fetching user and item features for (user, item) pairs when training a production recommendation model

get_historical_features

Offline feature retrieval for batch predictions

Predicting user churn for all users on a daily basis

get_historical_features

Online feature retrieval for real-time model predictions

Fetching pre-computed features to predict whether a real-time credit card transaction is fraudulent

get_online_features

feature views
entities
data source
project page
Data ingestion
datasets
features