LogoLogo
v0.42-branch
v0.42-branch
  • 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
      • Project
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • [Alpha] Saved dataset
      • Permission
      • Tags
    • 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
    • Starting Feast servers in TLS(SSL) Mode
  • Reference
    • Codebase Structure
    • Type System
    • Data sources
      • Overview
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • 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
      • Snowflake
      • Redis
      • Dragonfly
      • IKV
      • Datastore
      • DynamoDB
      • Bigtable
      • Remote
      • PostgreSQL
      • Cassandra + Astra DB
      • Couchbase
      • MySQL
      • Hazelcast
      • ScyllaDB
      • SingleStore
    • Registries
      • Local
      • S3
      • GCS
      • 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+
Powered by GitBook
On this page

Was this helpful?

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

Project

PreviousOverviewNextData ingestion

Last updated 5 months ago

Was this helpful?

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).

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.

The concept of a "project" provide the following benefits:

Logical Grouping: Projects group related features together, making it easier to manage and track them.

Feature Definitions: Within a project, you can define features, including their metadata, types, and sources. This helps standardize how features are created and consumed.

Isolation: Projects provide a way to isolate different environments, such as development, testing, and production, ensuring that changes in one project do not affect others.

Collaboration: By organizing features within projects, teams can collaborate more effectively, with clear boundaries around the features they are responsible for.

Access Control: Projects can implement permissions, allowing different users or teams to access only the features relevant to their work.

feature views
entities
data source
datasets
features