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

Was this helpful?

Edit on GitHub
Export as PDF
  1. Getting started
  2. Concepts

Overview

PreviousConceptsNextData source

Last updated 2 years ago

Was this helpful?

The top-level namespace within Feast is a . 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.

Project

Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev, staging, prod).

Projects are currently being supported for backward compatibility reasons. Projects may change in the future as we simplify the Feast API.

feature views
entities
data source
project
features
datasets