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 the configured dataset with supported load modes and schema evolution.

How It Works

Prepare Google Cloud

Choose the data and billing projects, enable the required APIs, select a BigQuery location, and pre-create the datasets you plan to use.

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 least-privilege access

Grant job permissions in the billing project, dataset-level read or write access for the connector role, and the documented bucket roles for destination staging.

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 require a default dataset and an existing customer-owned Cloud Storage staging bucket in a matching location.

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?

No. Cloud Storage staging is required only when BigQuery is used as a destination. The destination bucket must already exist and remains under your IAM, encryption, retention, and lifecycle controls.

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.

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. The official Docker agent includes the JVM module access needed for high-throughput reads. A custom JAR launcher must add --add-opens=java.base/java.nio=ALL-UNNAMED, and ADC must be supplied by the runtime when that authentication method is selected.

Deploy a self-hosted Docker Agent