LogoLogo
v0.12-branch
v0.12-branch
  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Entity
      • Feature view
      • Feature service
      • Feature retrieval
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • FAQ
  • Tutorials
    • Overview
    • Driver ranking
    • Fraud detection on GCP
    • Real-time credit scoring on AWS
  • How-to Guides
    • Running Feast with 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
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
  • Reference
    • Data sources
      • File
      • BigQuery
      • Redshift
    • Offline stores
      • File
      • BigQuery
      • Redshift
    • Online stores
      • SQLite
      • Redis
      • Datastore
      • DynamoDB
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • 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 Git
Export as PDF
  1. Getting started
  2. Concepts

Data source

PreviousOverviewNextEntity

Last updated 3 years ago

Was this helpful?

The data source refers to raw underlying data (e.g. a table in BigQuery).

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or when materializing features into an online store.

Below is an example data source with a single entity (driver) and two features (trips_today, and rating).

Ride-hailing data source