Search…
master
Introduction
Community
Roadmap
Changelog
Getting started
Quickstart
Concepts
Overview
Data source
Entity
Feature view
Feature service
Feature retrieval
Point-in-time joins
Architecture
Third party integrations
FAQ
Tutorials
Overview
Driver ranking
Fraud detection on GCP
Real-time credit scoring on AWS
How-to Guides
Running Feast with GCP/AWS
Running Feast in production
Upgrading from Feast 0.9
Adding a custom provider
Adding a new online store
Adding a new offline store
Adding or reusing tests
Reference
Data sources
Offline stores
Online stores
Providers
Feature repository
[Alpha] On demand feature view
[Alpha] Stream ingestion
[Alpha] Local feature server
[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
Data source
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
Previous
Overview
Next
Entity
Last modified 20d ago
Export as PDF
Copy link