Google BigQuery Connector

Use Google BigQuery as a source or destination for warehouse replication, analytics pipelines, and downstream activation workflows.

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Evaluating against Fivetran? See how Supaflow handles Google BigQuery pricing, connector quality, and Snowflake deployment side-by-side.

Supaflow vs Fivetran

Why Supaflow

All connectors included

Every connector is available on every plan. Pricing does not increase with connector count.

Pay for compute, not rows

Credit-based pricing. Usage scales with your pipelines, not with row counts.

One platform

Ingestion, dbt Core transformation, reverse ETL, and orchestration in a single workspace.

Capabilities

BigQuery as a Source and Destination

Replicate selected BigQuery data to another platform, or load data from supported Supaflow sources into BigQuery.

Managed Identity Choices

Use the recommended Supaflow-managed service account, a customer service-account key, or runtime-provided credentials on a private agent.

Full Refresh or Incremental Reads

Run full refreshes or use a reliable date or timestamp cursor to read new and updated source rows.

Flexible Destination Loads

Choose append, merge, overwrite, or truncate-and-load behavior, with merge as the default.

Customer-Owned Staging

Keep destination staging in a Cloud Storage bucket you own and control, with scoped IAM and lifecycle policies.

Supported Objects

Source Objects

Tables and external tables

Accessible BigQuery tables selected for full-refresh or cursor-based incremental replication.

Views and materialized views

Accessible BigQuery views that can be queried by the configured identity.

Destination Objects

BigQuery destination tables

Pipeline data loaded into pipeline-named datasets or the configured default dataset, with supported load modes and schema evolution.

How It Works

Prepare Google Cloud

Choose the data and billing projects, enable the BigQuery API, select a BigQuery location, and prepare an existing Cloud Storage bucket in that location. For destination loads, also prepare the default dataset.

Choose authentication

Use the recommended Supaflow-managed service account, upload a customer service-account key, or use Application Default Credentials on a private agent.

Grant required access

Grant BigQuery Job User and BigQuery Read Session User on the job project, BigQuery Data Editor on the data project, and Storage Object User and Storage Bucket Viewer on the bucket.

Configure and test

Enter the project, location, discovery, and destination staging settings that apply, then click Test & Save.

Use Cases

Consolidate analytics data in BigQuery

Load operational, SaaS, database, and file data into a governed BigQuery dataset for reporting and analysis.

Move warehouse data between platforms

Replicate selected BigQuery tables into another warehouse, database, or data lake during migrations and multi-platform projects.

Power downstream activation

Use BigQuery data as a source for reverse ETL and other downstream workflows supported by Supaflow.

Frequently Asked Questions

Can Google BigQuery be both a source and a destination?

Yes. Source connections read accessible BigQuery tables and views; destination connections load data into BigQuery tables. Setup requires an existing customer-owned Cloud Storage bucket for either role, and destination connections also require a default dataset.

Which authentication method should I use?

The Supaflow-managed service account is the recommended default and uses short-lived credentials. Customer service-account JSON is available for organizations that deliberately manage keys. Application Default Credentials is intended for private agents whose runtime already supplies ADC.

Does a BigQuery source require a Cloud Storage bucket?

Yes. Supaflow requires the GCS Staging Bucket field during setup for both source and destination connections and checks the bucket’s metadata and location. A source connection only checks the bucket; it does not stage data there. Use an existing customer-owned bucket in the configured location with the bucket roles listed in the setup guide.

Is source schema discovery enabled by default?

Yes, for new source connections. Destination connections leave discovery disabled by default, and existing connections keep their saved setting.

Configure BigQuery source discovery

Why must the dataset and staging bucket locations match?

The connector validates the configured BigQuery location against both the destination dataset and the Cloud Storage bucket. Cross-location destination loading is not supported.

Why does a BigQuery destination need project-level Data Editor?

The default pipeline namespace rules create datasets such as salesforce and salesforce_2 and then manage tables inside them. BigQuery Data Editor on the data project supplies both kinds of permission. For stricter dataset-scoped access, pre-create the default dataset and configure every pipeline to use Destination Defined namespace rules.

Does the BigQuery source support CDC or hard-delete detection?

No. The source supports full refresh and cursor-based incremental sync. It does not read BigQuery logs for CDC and does not automatically detect source hard deletes.

What does a private agent need to connect to BigQuery?

The agent needs outbound access to the required Google Cloud APIs and a supported connector identity. When using Application Default Credentials, the agent runtime must already provide those credentials. See the private-agent setup guide for deployment requirements.

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