BigQuery
Introduction
Faraday easily integrates with Google BigQuery, enabling you to embed AI-powered predictive analytics anywhere else in your stack. Discover your brand's bespoke personas, score your customers for churn risk, find repeat purchasers, and more, enabling you to confidently engage the right customers, at the right time, with personalized, relevant content. The best part? No code—and no PhD—required.
Getting started
Make sure you have a Faraday account (signup is free!) and that it's not in test mode.
Prerequisites
You'll need the following details to create your connection to BigQuery:
- Dataset name required
- Project ID required
Connection
Setting up your connection
First, you'll need to grant Faraday access to BigQuery.
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:
faraday-incoming@production-237317.iam.gserviceaccount.com
- Targets:
faraday-outgoing@production-237317.iam.gserviceaccount.com
- Give service account
BigQuery Job User
at the Project level - 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.
Creating your connection
Now you can connect Faraday to BigQuery.
Wait briefly while Faraday establishes your connection. It shouldn't take long.
Your new connection is now ready to use!