How to load data from Pardot to BigQuery

Learn how to use Airbyte to synchronize your Pardot data into BigQuery within minutes.

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Bespoke pipelines are:
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Pardot connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Pardot data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Pardot to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Export Data from Pardot

Begin by exporting the data you need from Pardot. Log in to your Pardot account, navigate to the relevant data set (such as prospects, lists, or campaigns), and use Pardot's native export feature. Choose CSV format for the export, as this is compatible with BigQuery's import process. Ensure you have the appropriate permissions to export data.

After exporting, review the CSV files to ensure they are correctly formatted for BigQuery import. Check for any inconsistencies, such as missing headers or incorrect delimiters. Clean the data by removing any unnecessary columns or correcting data types, if needed, to match the intended schema in BigQuery.

If you haven't already, set up a Google Cloud Project. Go to the Google Cloud Console, create a new project, and enable billing. Once the project is set up, enable the BigQuery API by navigating to the "APIs and Services" dashboard and activating it for your project.

In the Google Cloud Console, navigate to BigQuery. Create a new dataset where your Pardot data will be stored. Choose a unique dataset ID and configure the data location and expiration settings as needed. The dataset acts as a container for your tables.

Before importing data, define the schema for the BigQuery table that will store your Pardot data. This schema should match the structure of your CSV files. You can define the schema manually using the BigQuery UI or programmatically using SQL commands. Pay attention to data types and column names to ensure compatibility.

Upload your prepared CSV files to Google Cloud Storage (GCS), as BigQuery imports data from GCS. Create a bucket in GCS if you don't have one, and upload the files. Ensure the bucket and files have the necessary permissions set for BigQuery to access them.

Finally, load the data from GCS into BigQuery. Use the BigQuery Console, the `bq` command-line tool, or a SQL query to load your data. Specify the GCS file path, the target table in BigQuery, and the schema (if not defined in the table). Verify that the data loads correctly by checking for errors and validating the imported data against your original CSV files.
By following these steps, you can efficiently transfer data from Pardot to BigQuery without relying on third-party connectors or integrations.