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  • Introduction
  • Quickstart
  • Getting started
    • 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
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  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
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    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
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        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
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      • Learn Feast
    • Concepts
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      • Minimal Ride Hailing Example
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      • Getting online features
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  1. Reference
  2. Data sources

BigQuery

Description

BigQuery data sources allow for the retrieval of historical feature values from BigQuery for building training datasets as well as materializing features into an online store.

  • Either a table reference or a SQL query can be provided.

  • No performance guarantees can be provided over SQL query-based sources. Please use table references where possible.

Examples

Using a table reference

from feast import BigQuerySource

my_bigquery_source = BigQuerySource(
    table_ref="gcp_project:bq_dataset.bq_table",
)

Using a query

from feast import BigQuerySource

BigQuerySource(
    query="SELECT timestamp as ts, created, f1, f2 "
          "FROM `my_project.my_dataset.my_features`",
)
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Last updated 3 years ago

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