What permissions does the service account need?

You must grant two IAM roles on the source project: **BigQuery Data Viewer** (to list datasets, tables, and read their data) and **BigQuery Read Session User** (to open read sessions for fetching rows). When you click **Save**, Renta lists the tables of the selected dataset. If permissions are missing, the connection fails right away.

Can a single connection export tables from multiple datasets?

No. A connection is scoped to a single Google Cloud project and dataset. To export tables from another dataset or project, you must create an additional Google BigQuery connection.

Can I export a BigQuery view?

No. Renta reads data via the BigQuery Storage Read API, which only supports tables. To export a view, you must first materialize it into a table.

Does Renta run SQL queries in my project?

No. Renta reads rows directly through the BigQuery Storage Read API, so pipeline runs do not create query jobs and do not consume billable query bytes.

Why did my run fail with a message about a changed table schema?

A column configured in the pipeline no longer exists in the source table. To resolve this, open the pipeline settings, review the **Table columns** list, and save the pipeline with the currently available columns.

I deleted all rows in the source table. Why does the destination table still contain data?

A pipeline run that finds zero rows skips the destination write, so the table keeps the data from the previous successful run. In all other scenarios, the run replaces the destination table with the current contents of the source table.

Google BigQuery

Renta replicates tables from your Google BigQuery dataset to any supported destination.

Each data pipeline loads a single table. You select which columns to replicate, and Renta reads the rows through the BigQuery Storage Read API without running SQL queries in your project.

Authentication

Renta authenticates with Google BigQuery using a Google Cloud service account. Create a service account in your Google Cloud project and grant it two IAM roles:

BigQuery Data Viewer.
Allows Renta to list datasets and tables and read their data.
BigQuery Read Session User.
Allows Renta to create read sessions for fetching rows.

Generate a JSON service account key and download the file. For instructions on creating service accounts and keys, refer to the Google Cloud IAM documentation.

When you click Save, Renta validates your access by requesting the list of tables in the selected dataset. A key with insufficient permissions returns an error immediately instead of failing on the first pipeline run.

Connect Google BigQuery as a source

Follow these steps to add Google BigQuery as a source in Renta using a Google Cloud service account key.

Go to the CatalogStep 1

In the Renta console, navigate to Connections > Catalog in the left sidebar.
Expand the Database category and click the Google BigQuery card labeled Source. The card labeled Destination connects the data warehouse you load data into.

Google BigQuery Source card in the Database category of the Renta connection catalog

Upload the service account keyStep 2

The authorization method is set to GCP Service Account. Click Upload a Service Account JSON file and select the key file you downloaded from the Google Cloud Console.

Add a new Google BigQuery connection page with the GCP Service Account method and the key upload button

Choose the project and datasetStep 3

Source name.
Enter a name for this data source. This name is used exclusively in the Renta console.
BigQuery project.
Select the Google Cloud project you want to export data from. The dropdown lists all projects accessible by your service account.
BigQuery dataset.
Select the dataset within the project. Pipelines using this connection read tables from this dataset only.

Click Save. Renta verifies your access by listing the tables within the dataset.

Google BigQuery connection form with the source name field and the project and dataset dropdowns

Verify the source creationStep 4

The new source appears in your Sources list with the Active status. You can now use it to build data pipelines.

Sources list with the new Google BigQuery source in the Active status

Create a data pipeline

Once your BigQuery source is connected, you can create a pipeline to load a table into your destination.

Create a data pipeline

Link your Google BigQuery source to a destination and configure the data pipeline.

Select Google BigQuery as the sourceStep 1

In the left sidebar, click New pipeline. On the Source step, choose Google BigQuery under Already connected, and then click Next step.

Select a source step with Google BigQuery under Already connected

Select your connectionStep 2

Select the Google BigQuery connection you created previously and click Next step. The connection card displays the service account email, which is helpful if you manage multiple BigQuery connections.

Select your Google BigQuery connection step with the service account email on the connection card

Select a destinationStep 3

On the Destination step, select your target destination and click Next step. Google BigQuery is also available as a destination, allowing you to copy tables across different projects or datasets.

Select a destination step with Google BigQuery under Already connected and other warehouses below

Select the destination connectionStep 4

Choose the destination connection you want to load data into and click Next step. The card displays the target dataset and project.

Select your destination connection step with the dataset and project identifiers on the card

Pipeline configuration

In the final step, you configure the table, columns, and execution schedule.

Table selection

In the Table dropdown menu, select the table you want to replicate. The list shows the tables of the dataset you specified during connection setup. One pipeline loads one table, so create a separate pipeline for each table you need.

Google BigQuery pipeline settings with the Table dropdown set to orders and the selected table columns below

The dataset is bound to the connection, not the pipeline. To export a table from a different dataset or project, create another Google BigQuery connection.

The dropdown menu also lists views, but Renta exports native tables only. A pipeline run over a view fails with an error.

Column configuration

Select the columns you want to load into the destination table. When you open the picker, Renta fetches the schema directly from the table. Use Select All to choose every column, or remove unneeded columns from the Selected Table columns list.

Table columns picker with the columns of the orders table, the Selected tab, and the Select All button

BigQuery column type	Replication behavior
RECORD	Selected in its entirety, including all nested fields.
REPEATED	Loaded as an array of values into a single destination column.
Numeric, date, and boolean	Keep their types in the destination.
TIMESTAMP, DATETIME, TIME	Loaded as datetime values.
NUMERIC, BIGNUMERIC	Loaded as float values.
STRING, BYTES, JSON, GEOGRAPHY	Loaded as text.

Pipeline naming

Assign a name to the pipeline and specify the destination table name.

Google BigQuery integration name and table name fields

Integration name.
The display name of the pipeline in the Renta console.
Table name.
The name of the table in your destination. You can keep the auto-generated name or enter your own.

Schedule execution

Define how frequently the pipeline should run.

Google BigQuery schedule type selection with the Daily option checked

Schedule type	Description
Manual	The pipeline runs only when triggered manually via the Renta console or API.
Daily	The pipeline runs once a day at a specified update time.
Interval	The pipeline runs at specified time intervals.
Cron Expression	The pipeline runs on a schedule defined by a cron expression.

For Daily schedules, set the Update time (the exact time each run starts), then click Create pipeline.

Update time selection and the Create pipeline button

Renta also supports API triggers and integration with external orchestrators such as Apache Airflow, Dagster, and Prefect. See the scheduling documentation for details.

The new pipeline appears in the Pipelines section under the ETL tab, where you can track its runs, progress, and the destination table name.

Pipelines list with the new Google BigQuery integration in the Completed status

Sync strategy

Renta synchronizes the selected BigQuery table with a single strategy.

Strategy	Supported report types	Description
Full reload	The selected table	Renta reads the table in full on every run and replaces the contents of the destination table, so it matches the source at the time of the run. Renta fetches rows through the BigQuery Storage Read API and does not run SQL queries in your project.

Because every run reads the table in full, the pipeline has no date range or overwrite period settings, and destination tables stay unpartitioned.

Frequently asked questions

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