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Google BigQuery Pipeline Setup Guide: Export Marketing Data to BigQuery

This guide walks you through creating an automated data pipeline that exports your marketing and sales data to Google BigQuery.

Prerequisites

Before you begin, make sure you have:

  1. An Adzviser account with at least one connected data source (Sign up here)
  2. A Google Cloud project with BigQuery API enabled (Enable BigQuery API)
  3. A GCP service account with BigQuery Data Editor and BigQuery Job User roles

Your account also needs access to this warehouse destination and available monthly row capacity. Check Usage metrics from the account menu on Set Up before a large backfill.

Step 1: Create a New Pipeline

Navigate to the Set Up page on Adzviser and select Google BigQuery as your destination. Click + Create New Pipeline to begin.

Create a new Google BigQuery pipeline in Adzviser

Step 2: Set Up a GCP Service Account

In the Google Cloud Console, create a service account (or use an existing one) and ensure it has the following roles:

  • BigQuery Data Editor — allows editing all the contents of datasets
  • BigQuery Job User — allows running jobs (required for data loading)

Then navigate to Keys and click Add key > Create new key to generate a JSON key file.

Set up a GCP service account with BigQuery Data Editor and Job User roles
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Store your service account JSON key securely. It grants access to your BigQuery project.

Step 3: Enter Your Service Account JSON Key

Open the downloaded JSON key file and copy the entire contents. Paste it into the Service Account JSON field in Adzviser.

The Dataset Prefix is combined with your workspace and data source to form each dataset name. See dataset and table names for an example.

You can also configure the Dataset Location (e.g., US, EU) — this determines where your BigQuery datasets are physically stored and cannot be changed after creation.

Click Test to verify the connection, then click Save.

Copy your GCP service account JSON key and paste it into the Adzviser setup form

Step 4: Select Data Source and Accounts

Choose which data source you want to export (e.g., Facebook Ads, Google Ads, Shopify) and then select the specific accounts to include in this pipeline.

Select your data source and accounts for the BigQuery export pipeline

Step 5: Configure Metrics and Export Mode

Select the metrics and breakdowns you want to export. Then choose your export mode:

Option A: Run Once (Backfill)

Select Run Once to perform a one-time data export. Choose your date range and granularity, then click Next to review your configuration. Click Start Export to begin the backfill.

Configure Run Once mode to backfill historical data to BigQuery

Option B: Scheduled Export

Select Schedule to set up automatic, recurring exports. Configure the frequency (e.g., Daily), date range per run (e.g., Yesterday), and the time of day to run. Click Next to review, then click Save Schedule to activate.

Configure Schedule mode for automatic recurring exports to BigQuery

Each pipeline runs on its own schedule. Pipelines with the same destination, workspace, source, and report segment can share a table and replace each other's rows for overlapping dates. See pipelines that share a table.

Monthly row capacity

Both Run Once backfills and scheduled exports count toward your account's Monthly active rows (MAR) allowance, shared by Amazon S3 and BigQuery. Successfully exported data rows count; headers and unsuccessful writes do not. Exporting the same rows again counts again, even when they replace existing data.

To check MAR, open Set Up, open the account menu, and select Usage metrics. The MAR panel shows exported rows for the current UTC calendar month. It does not show the plan limit or capacity reserved by exports in progress. Review Subscription info or contact Adzviser to confirm your account's allowance.

An export can be rejected for capacity even when its connection settings are correct. If capacity is reserved by running exports, wait for them to finish before retrying. If the export needs more rows than remain, reduce its date range or detail, wait for the next monthly period, or contact Adzviser about more capacity. Splitting a backfill does not increase the monthly allowance.

Check Export History before retrying a failed run: partial writes may already have counted toward MAR. See Usage metrics and capacity recovery for the exact error messages and next steps.

Next Steps

Once your pipeline is running:

  • Monitor exports — check the Export History tab for run results. It does not keep a separate table snapshot for each run; see keeping earlier data.
  • Query your data — open BigQuery Console and run SQL queries on your exported tables
  • Add more pipelines — create additional pipelines for other data sources or accounts
  • Build dashboards — connect BigQuery to your favorite BI tools

Need help? Contact us at https://adzviser.com/contact-us.