Connect Google Ads to BigQuery

Using Windsor.ai connector to import data from Google Ads into BigQuery 

While most available methods of syncing Google Ads data with BigQuery can be complex and require technical expertise, Windsor.ai simplifies the process with a no-code, user-friendly ELT connector. With just a few clicks, you can load your Google Ads data into BigQuery, eliminating the need for manual data preparation, formatting, and transfers.

With Windsor.ai, you can:

• Automate Google Ads reporting and avoid manual exports, saving time.
• Access real-time insights on key metrics like ad performance, ROI, and customer engagement.
• Identify trends and gaps to refine your advertising strategy.
• Use customizable Google Ads dashboard templates for an in-depth overview of your performance.

Join over 3,000 businesses that trust Windsor.ai to streamline Google Ads integration with BigQuery. Gain actionable insights to optimize ad budget allocation and boost campaign effectiveness.

How to connect Google Ads to BigQuery with Windsor.ai

Check out our video tutorial and a step-by-step guide to easily send your Google Ads data to BigQuery using Windsor.ai.

1. Register

Create a new account or log in if you already have one.

2. Select your data source

Choose Google Ads as your data source and grant access to Windsor.ai.

3. Select your destination

Set Google Ads as the destination for your data.

4. Create a destination task

Click on the “Add Destination Task” button and fill out the required fields.

5. Sync your data

In the final step, grant access to the user: [email protected].

That’s it! Now, your Google Ads data will start syncing and automatically populate into your BigQuery account.

*Note: As the connector URL, you can use any URL providing a JSON—whether from connectors or a URL with cached and transformed data.

How to connect Google Ads to BigQuery manually

Manually integrating Google Ads with BigQuery involves multiple steps, including API setup, data extraction, transformation, and loading. While this approach requires technical expertise, it provides full control over the data pipeline.

Windsor.ai offers a seamless alternative by automating the entire process, eliminating the need for manual scripts, ensuring real-time data synchronization, and reducing errors.

However, if you prefer a manual integration, follow this step-by-step guide to extract Google Ads data via the Google Ads API and load it into Google BigQuery.

Prerequisites

Before proceeding with the manual integration, ensure you have the following:

• A Google Ads account with API access enabled.
• Google Ads API credentials (Developer Token, Client ID, Client Secret, and Refresh Token).
• A Google Cloud Platform (GCP) account with BigQuery enabled.
• A Service Account with the necessary permissions for BigQuery.

Step 1. Set up Google Ads API

1. Enable Google Ads API access:

• Log in to the Google Ads API Developer Console.
• Register for API Access and obtain a Developer Token.

2. Create API credentials:

• Go to Google Cloud Console → APIs & Services → Credentials.
• Create an OAuth 2.0 Client ID for API authentication.
• Generate a Refresh Token to access the Google Ads API using the created credentials via cloud.

3. Get required credentials:

• Developer Token from Google Ads API Console.
• Client ID and Client Secret from Google Cloud Console.
• Refresh Token from Google OAuth Authentication.
• Google Ads Customer ID for data extraction from Google Ads account.

Step 2. Extract Google Ads data

Use the Google Ads API to fetch campaign data. The API query language Google Ads Query Language (GAQL) allows you to retrieve relevant insights.

1. First get the Access Token needed to authorize the Google Ads API request. You can get it using the following API call.

2. You can extract Google Ads data by using Google Ads API. You can edit the query to get what data you want from the API.

You’ve set up Google Ads API and got your campaign data. Now, let’s get this data in the BigQuery table.

Step 3. Set up Google BigQuery

1. Enable BigQuery API:

• Go to the Google Cloud Console.
• Select your project or create a new one.
• Navigate to APIs & Services -> Library.
• Search for BigQuery API and click Enable.

2. Create a BigQuery Dataset and Table:

• Go to the BigQuery Console.
• Click on your project in the left panel.
• Click Create Dataset and provide a name.
• Click Create Table inside the dataset.
• Define the schema based on your data structure.

3. Create a Service Account for BigQuery:

• Navigate to IAM & Admin -> Service Accounts.
• Click Create Service Account.
• Assign BigQuery Admin role.
• Click Create Key and select JSON format.
• Download the key file for authentication.

Step 4. Load data into BigQuery

1. You can upload the data to the BigQuery table using the BigQuery API URL. 

Here is a sample API URL that is called via the curl command. Replace {PROJECT_ID}, {DATASET_ID}, and {TABLE_ID} with your actual BigQuery details and “google_ads_data.json” file with your ads data file.

2. You can go to your GCP console and query the data from your BigQuery table to verify that Google Ads data is present.

Cheers, you’ve sent your Google Ads data to Google BigQuery!

FAQs

What is Google Ads?

Google Ads is a leading online advertising platform that allows businesses to create and display advertisements across Google’s network, including search results, high-traffic websites, and apps. The platform operates primarily on a pay-per-click (PPC) model, where advertisers bid for ad placements and pay only when users click on their ads.

Google Ads also provides comprehensive targeting options, allowing advertisers to reach their audience based on factors like location, keywords, demographics, and user behavior. Additionally, the platform integrates seamlessly with other Google services, such as Google My Business and Google Analytics, giving you a more detailed look into the performance of your ad campaigns.

What is BigQuery?

BigQuery is a fully managed data warehouse provided by Google as part of the Google Cloud Platform (GCP), designed for large-scale data analytics. Its serverless architecture removes the need for infrastructure management, allowing users to focus on querying and analyzing data.

With its highly scalable framework, BigQuery can handle datasets on the petabyte scale and perform lightning-fast queries. Additionally, powered by Google’s AI tools, BigQuery allows users to execute machine learning tasks directly within the platform, streamlining advanced analytics workflows.

Do you have helpful links to get started with integrating Google Ads and BigQuery using Windsor.ai?

Yes, we have helpful resources to help you get started. You can explore our official documentation and ready-to-use templates for seamless Google Ads to BigQuery integration with Windsor.ai:

What prerequisites do I need to integrate Google Ads with BigQuery?

You need a Google Ads account with API access, Google Ads API credentials, a Google Cloud Platform (GCP) account with BigQuery enabled, and service account credentials for BigQuery.

What should I do if my BigQuery data upload fails?

Here are things you can look into:

• Ensure your service account has the correct permissions.
• Verify the JSON format of the uploaded file.
• Check if the dataset and table names in the API request are correct.

What should I do if my API request fails?

Check the following things:

• Ensure your API credentials are correct.
• Verify that your OAuth token has not expired.
• Check if your Google Ads account has API access enabled.

Can I use BigQuery data transfer service to connect Google Ads to BigQuery?

While BigQuery’s data transfer service offers some advantages for connecting Google Ads to BigQuery, there are several limitations to consider:

• Limited historical data transfer: The data transfer service can only backfill data for up to 180 days. If you need historical data beyond this period, you’ll need to handle the data transfer manually, which can be time-consuming and may require additional technical resources.
• Technical expertise required: Setting up and managing the data transfer process typically requires specialized technical knowledge. If your team lacks the necessary skills, you may need to invest in training or allocate technical resources, which can be costly.
• Location compatibility: BigQuery does not allow you to combine datasets stored in different locations. Therefore, it’s crucial to ensure all datasets are in the same location at the time of setup, as this can’t be changed later.
• Limited data transformation capabilities: The data transfer service is focused primarily on transferring data, not transforming it. For example, if you need to adjust timestamps (e.g., converting UTC to PST), you will need to handle this transformation separately, either before or after the transfer.
• Limited data source support: The data transfer service is specifically designed to transfer data from Google products. If you need to integrate data from other sources, such as Salesforce, Mailchimp, or Intercom, you will need to use alternative methods or services.

For a more powerful, flexible, and cost-effective solution, consider using a dedicated data integration platform like Windsor.ai. Our ELT service provides a user-friendly interface and a powerful data connector that enables you to integrate Google Ads with BigQuery in under 5 minutes, making the process quick and hassle-free.