Create a connection between Faraday and Google BigQuery so that your data is always up to date to make predictions, and your predictions can seamlessly sync back to your warehouse.


In this tutorial, we'll show you how to:

  • Connect your BigQuery account to Faraday using a connection.

Let's dive in.

  1. You'll need a Faraday account — signup is free!


You'll need the following details to create your connection to BigQuery:

  • Dataset name requiredtext
  • Project ID requiredtext

Granting access

First, you'll need Faraday access to your BigQuery account.

BigQuery is a serverless data warehouse. Access is shared using Google Cloud IAM permissions. We suggest that you create a Faraday-only dataset to both send and receive data. Within this dataset, Faraday would have full read and write access. Alternatively, you can give Faraday access to certain tables in a shared dataset.

Which IAM account (or both) depends on use of Targets and/or Datasets:

  • Datasets:
  • Targets:
  1. Give service account BigQuery Job User at the Project level
  2. Give service account BigQuery Data Owner at the Dataset level

Faraday suggests that you use an unguessable string somewhere in the path to your data. This avoids what is called the Confused deputy problem

For example, instead of naming an S3 bucket s3://faraday-acme/, name it s3://faraday-acme-pwiiprz162ez. This guarantees that malicious actors cannot guess the name and request that Faraday import data from it into their account. The same logic applies to any path that is used to locate data.



Use a POST /connections request:

curl --json '{
  "name": "BigQuery",
  "options": {
    "type": "bigquery",
    "dataset_name": "...",
    "project_id": "..."
  1. Wait briefly while Faraday establishes your connection. It shouldn't take long.

Your new connection is now ready to use.