How to Upload Google Ads Data to BigQuery in 2024

When analyzing the effectiveness of Google advertising campaigns in the Google Ads platform interface, you might feel the pain of not being able to compare the performance with other advertising services.

If you decide to analyze advertising performance in Google Analytics 4, most likely, you’ll get the data sampling, aggregation, or other GA4 interface restrictions.

Fortunately, this problem can be solved easily by uploading raw data from your advertising service to BigQuery and visualizing and analyzing reports in Google Sheets or Looker Studio.

In one of the previous articles, we’ve covered the 5 most common reasons for implementing a data warehouse for marketing reporting and start using BigQuery for storing and preparing the data for analysis.

To set up these data transfers, you must use the Google Cloud Console to create a new Google Cloud service account, enable APIs and services, and create credentials. When setting up the data transfer, choose Google Ads as the source and enter your Google Ads customer ID for authentication.

In this article, you will learn 3 methods to upload raw data from your Google Ads account to BigQuery and what you can do further to boost analytics capabilities.

Note: This post was originally published in December 2019, when GA Universal was live, and we completely updated it in June 2024 for accuracy and comprehensiveness on Google Ads and the state of GA4 and BigQuery.

Raw data from Google Ads represents the detailed, unprocessed information generated by your advertising campaigns on the platform.

This includes metrics like clicks, impressions, cost-per-click, click-through rates, conversion data, and other performance indicators over the dimensions like campaign, ad group, keyword, or date.

It also covers more granular details such as the time of clicks, geographical location of users, types of devices used, and specific keywords or ad placements that triggered the ad display.

This raw data, potentially, provides a comprehensive view of campaign performance, user interactions, and the overall effectiveness of your advertising efforts on Google Ads if you prepare and visualize it correctly.

Google BigQuery is a powerful, fully managed data warehouse designed for large-scale data analysis.

It allows businesses to store and query vast amounts of data quickly and efficiently using SQL. BigQuery is used for real-time analytics, machine learning model training, and generating business insights from complex datasets.

Its serverless architecture eliminates the need to manage infrastructure, enabling users to focus on extracting valuable insights from their data, optimizing performance, and making informed, data-driven decisions.

Raw data from Google Ads will allow you to analyze advertising campaigns with accuracy down to any dimension - keyword for example. Using the Google Ads API, you can retrieve raw data for more detailed analysis, giving you direct control over data retrieval and the ability to manage concurrent requests, backfill data set precise schedules, and target specific data points and reports.

By uploading raw Google Ads data to BigQuery, you can:

1. Build detailed reports without being limited by GA4 or any other restrictions;
2. Evaluate ad campaign effectiveness at the session or user levels;
3. Build cross-channel reports for better visibility;
4. Analyze KPIs that matter for you: ROAS, ROI, LTV, CAC, and CRR by region, user type (new or returned), device, and any other parameter you wish;
5. Manage your bids effectively;
6. Create custom remarketing audiences for more revenue;
7. Apply machine learning for more accurate attribution modeling that suits your business goals.
8. Evaluate the advertising performance based on the sales from the CRM system, not just based on online data from GA4;

Connecting Google Ads to BigQuery allows for seamless integration and comprehensive analysis of advertising performance.

This integration enables businesses to store, query, and analyze large volumes of ad data in real time, facilitating data-driven decision-making.

Additionally, you can create detailed reports, identify trends, and optimize ad campaigns more effectively.

To understand which campaigns, ads, and keywords bring buyers to your site, you need to combine data from Google Ads, Facebook Ads, Linkedin Ads, and other advertising services, maybe affiliates, as well as your website analytics tool.

The best place to merge all of those data for comprehensive reporting is Google BigQuery.

There are 3 methods to upload raw data from Google Ads: OWOX BI Pipeline, BigQuery Data Transfer and Ads Script.

Which way you choose depends on your budget, level of technical expertise, required accuracy, data quality, and data freshness for marketing analytics.

Configuring a Google Ads to Google BigQuery data pipeline in OWOX BI enables seamless data integration for advanced analytics.

OWOX BI Pipeline is designed for marketers and data professionals to seamlessly connect data from multiple sources into a centralized location - Google BigQuery.

The setup takes less than a minute, and you don't need to worry about data quality and freshness ever again.

1. Analytics-Ready Data: OWOX BI ensures that the data transferred to Google BigQuery is prepared and structured in a way that's immediately useful for analysis, reducing the time needed for data cleaning, normalizing, and preparation.

2. Zero Coding Required: The no-code approach makes it accessible to marketers and analysts without technical expertise, democratizing data analytics.

3. Historical data is available for 3 months. You can backfill data for a specified period, making historical analysis possible.

4. Comprehensive Data Integration: With support for 150+ data sources, OWOX BI allows businesses to aggregate data across various marketing channels, providing a holistic view of marketing performance — everything from a single platform.

5. No-code Customizable Data Transformation Templates: OWOX BI offers templates for blending ads data from multiple platforms in the same table, merging with on-site analytics data, building attribution models and so much more. All SQL-based, but without any coding required.

6. Data Ownership: Unlike other tools, OWOX BI doesn't store your data. Instead, move your data to your data warehouse. Making it suitable for businesses concerned about privacy regulations.

7. Zero learning curve: Last but not least. Setup takes less than a minute.

8. Setup Assistance: OWOX will help you set up the pipeline and ensure you receive all your data on time and in full.

9. Support: If there are any errors, data discrepancies, or missing data, you can get help from the OWOX Support team.

10. Fully Automated Updates: Any changes in the data structure from the service API are automatically reflected in subsequent transformations.

Step 0: Start OWOX BI FREE Trial;

Step 1: On the Workspace page, click the ‘New’ button and select the 'Pipeline' option from the drop-down menu.

Step 2: As a source, select ‘Google Ads.’

Step 3: Choose the pipeline 'Google Ads → Google BigQuery' and click the 'Create & Setup' button.

Step 4: The new pipeline is successfully created in ‘Draft’ status. To start data import, configure pipeline settings, including access to the source, and destination dataset.

Step 5: Set up source data:

5.1. Click on the ‘Source access’ section.

5.2. In the opened dialog, click “Continue with Google” to provide access for the OWOX BI app to your Ads account

5.3. In the Google sign-in window, choose the account you use with Google Ads.

5.4. Provide access to the OWOX BI app by checking all the checkboxes and pressing the 'Continue' button.

5.5. After successfully granting access, you will see a list of Ads accounts organized under MCC accounts. Select the Ads account from which you wish to import costs.

5.6. Click the ‘Save’ button to apply changes and close the dialog.

A green marker in the ‘Source access’ section confirms successful setup.

NOTE: If you need to import costs from more than one Ads account, you must create a new pipeline for each account. A single user in the OWOX BI project can create up to 100 Google Ads → Google BigQuery pipelines. Need more? Contact our Support team at bi@owox.com.

Step 6: Set up destination dataset:

6.1. Click on the ‘Destination dataset’ section.

6.2. In the opened dialog, choose a shared dataset from the list or click ‘Grant access…’ to add a new dataset following the instructions.

Optionally, after providing access to the GCP project, create a new dataset in the selected location.

Click the ‘Grant Access’ button to create a new dataset and provide access to it.

6.3. Select your dataset in the list of shared datasets, then click the ‘Save’ button to close the ‘List of shared’ dialog.

6.4. If everything is executed correctly, the dialog will automatically close. Therefore, a green marker with the name project.dataset.table will appear in the "Destination dataset" section.

(Optional) Step 7: Choose the tables and fields you wish to import. Click on the section:

In the dialog that appears, you'll find a complete list of tables available for import. All Google Ads costs are included in 5 tables, already selected by default. With the OWOX BI Pipeline, you have the option to import additional Google Ads data. Learn more

Optionally, select the tables you need from the list and then click the ‘Save’ button.

(Optional) Step 8: To adjust the Refresh window, click on the section:

In the dialog that appears, select a period of up to 30 days back for updating your costs. Learn more

To confirm your changes, click the 'Save' button.

Step 9: Activate the pipeline by clicking the ‘Activate’ button.

The pipeline status will change from ‘Draft’ to ‘Active.’

Note: OWOX BI also updates the imported data during the preset actualization window if cost data changes retrospectively in your ad account ✅

Each day, your data will be available in the Google Ads Account at 3:00 AM in the timezone of the account. Therefore, in the pipeline settings, we recommend scheduling the pipeline run between 3:00 and 4:00 AM in the same time zone.

Read more about when OWOX BI starts importing data from ad service to Google BigQuery

The pipeline has been successfully created. Click the ‘Get data for the past period’ button to retrieve historical data. Select the start date and click the ‘Run once’ button.

The pipeline will start a manual run to import historical data.

NOTE: While the pipeline is in execution (status: Running), you cannot manually initiate another run. You must wait for the current run to complete before starting a new one.

After finishing the run, open your Google BigQuery dataset to check the resulting data.

View all pipeline runs on the ‘Run history’ tab.

The Google Ads Customer ID is crucial for setting up data transfers as it identifies the specific account for which data will be transferred.

Before we begin, let’s talk about the accounts and access rights you need to have. The Google Ads data transfer process involves selecting Google Ads as the source, entering the Customer ID, and configuring the transfer settings, which ensures seamless data integration and reporting.

To create authentication credentials for accessing data from Google's BigQuery and Google Analytics APIs, you will need a service account JSON file. This file contains the necessary credentials for the application or service to communicate with the respective APIs.

To set up the data collection using any of the options, your Google account must be granted both the BigQuery Data Editor and BigQuery User roles for the destination project.

Otherwise, BigQuery won’t let you upload the data. To check/grant the BigQuery permissions, open the GCP console and select IAM and admin — Manage Resource from the side menu. Then select the project and click Add Principals.

Enter the user’s email, select the BigQuery Admin role, and save your changes.

Also, please ensure your Google account has one of these access levels: ‘Read-only, ‘‘Standard, ‘or ‘Admin.‘ If you don’t have specified access permissions, then contact the administrator of your Google Ads account.

Google BigQuery Data Transfer Service automates data movement from Google Ads (and other sources) into BigQuery, making it easier to analyze advertising data alongside other business data.

Here are the pros and cons of using the BigQuery Data Transfer Service for loading Google Ads data into BigQuery:

1. Native integration with Google Ads;
2. Google Cloud's strict security protocols ensure that data is transferred according to privacy regulations.
3. Data Transfer is 100% free; you only pay for data processing in BigQuery.

• The initial setup is complex and might require a learning curve, especially for users who are not familiar with Google Cloud Platform services.
• Responsibility: You will need to handle data import configuration, connection setup, and status monitoring.
• Independent Troubleshooting: Any data-related issues must be addressed on your own.
• Import Limits: Data Transfer allows importing up to 300 days of data simultaneously, with each day imported in 30-minute intervals. Importing a year’s worth of data can take about 7 days.
• Scalability Issues: This can be problematic for many accounts with extensive dates to backfill or multiple reports needed.
• Data latency: There can be a delay between the time data is generated in Google Ads and when it becomes available in BigQuery, which might not be suitable for real-time analysis needs.
• No customization options: The automatic transfer offers limited options for customization. Users might need to perform additional steps for specific data requirements or formats.
• The data is transferred in a format not compatible with other advertising services, so you might need additional transformations in BigQuery to be suitable for specific analysis purposes.

If you already have a project in GCP, just skip the first step.

If not, open the GCP console and select IAM and admin — Manage Resource from the side menu. Click the Create Project button. Then enter the project name, specify the organization, and click Create:

Be sure to enable billing. To do this, open the Billing in the side menu, select the project, and link Billing Account:

Next, complete all the fields by entering your contact information and payment card details. If this is your first project in GCP, you will receive $300 that can be used for 90 days. When you exhaust this limit, you don't need to return the money. For further use, you just refill the balance on the card that you linked to the project.

After you create a project, you must activate the BigQuery API. To do this, go to APIs & Services — Dashboard from the GCP side menu, select the project, and click Enable APIs and Services.

In the API library, search for "BigQuery API" and click Enable:

To use the API, click Create Credentials:

From the drop-down list, choose the BigQuery API and click User data/Application data according to your need.

Create the name of the service account and specify the service description. Click Create and Continue:

Grant the service account access to the project, choose the appropriate role, and proceed by clicking Continue.

Provide users with access to the service account, specify the email ID associated with the service account for role assignment, and then click "Done."

Next, you need to activate the data service in BigQuery. To do this, open GBQ and select Data Transfers from the side menu on the left. Then, enable the BigQuery Data Transfer API:

In the BigQuery, select the project and click the Create Dataset button on the left. Complete all required fields for the new dataset (name, location, retention):

Click the Transfers tab on the side menu, and then click Create Data Transfer. Then select Google Ads (formerly AdWords) for the source and enter the name of the upload, for example, Data Transfer.

Under Schedule options, you can leave the default set to Start Now or set the date and time you want to start downloading and it repeats every 24 hours.

Then, you have to specify the GBQ dataset to load reports from Google Ads into. Enter your Customer ID (this is the ID of your Google Ads account or MCC ID) and click Add. You can view the Customer ID in your Google Ads account in the upper right corner, next to your email. Then specify table filters for loading, and set the conversion date for AdWords to Google Ads transition.

Various types of Google Ads reports can be transferred to BigQuery, including campaign performance, keyword performance, and conversion reports. These reports can be exported using CSV files, Google Ads Scripts, or the BigQuery Data Transfer Service for Google Ads.

Then give the refresh window duration along with the relevant service account. And then, you need to authorize the Gmail account you are using. Toggle on Pub/Sub notifications and either select an existing Cloud Pub/Sub topic or create a new one to configure run notifications for your transfer. The following day, the information will appear in the dataset you specified when you set up the transfer.

As a result, you will receive a large amount of raw data in GBQ that you can work with: tables by campaigns, audiences, common (custom) tables, keywords, and conversions. For example, if you want to build a custom dashboard, you can pull non-aggregated data from these tables.

There is basically one benefit here - It's totally free.

However, here is why it might not be the best option for you:

1. You cannot upload historical data. You can only get the information from the previous day.
2. The most complex from the technical point of view and requires a lot of manual work.
3. It might be time-consuming if you want to set up uploads for a large number of ad accounts.
4. You will need to make script changes manually for each account. At the same time, there is a high risk of making a mistake.

Here is how you can collect Google Ads data into BigQuery using Ads Script (by Apps Script).

Open your Google Ads account, navigate to the left sidebar, select "Tools," then choose "Bulk Action," go to "Scripts," and click the plus symbol to add a new script.

Then, in the upper-right corner, click the Advanced APIs button, select BigQuery, and save your changes:

Be sure to sign up with the account you signed in to Google Ads with:

Copy the script below. In the BIGQUERY_PROJECT_ID, BIGQUERY_DATASET_ID, and Your email lines, replace the values with your own information: project name, GBQ dataset, and email. Paste the script text into the text editor.

return html.join('\n');

Before running the script, please make sure to click the Preview button in the lower right corner to check the result.

If there are errors in it, the system will caution you and indicate in which line it occurred, as in this screenshot:

If there are no errors, click the Run button:

As a result, you will receive a new CLICK_PERFORMANCE_REPORT report in your GBQ that will be available the next day:

Recall that when you use Data Transfer, you get a large amount of raw, non-aggregated data. With Ads Script, you will only have information about certain fields.

Now you need to combine the data from Google Ads with the data from other adveritins sources, as well as with online user behavior data in order to understand which campaigns were the most profitable and which ones require tweaking.

Please note that the tables you get in the BigQuery, for example, with Data Transfer, don't have a Client ID parameter.

Whether you are using GA4 Export to BigQuery or OWOX BI Streaming for web analytics tracking, we are here to help you with the set of no-code customizable templates to build advanced analytics for your business in just 1 day.

Tip 1. With Data Transfer, you can upload historical data from Google Ads to BigQuery. At the same time, there are no restrictions on the total period of loading (either for a year or for three), but with data for only 180 days at a time.

You can activate the upload and specify the period using the Schedule Backfill button on the Transfers tab by selecting the transfer you want:

Tip 2. If you want to check the number of Google Ads accounts for which GCP will charge, you need to determine the number of ExternalCustomerID in the Customer table using this query.

You can edit the dates in the query.

Tip 3. You can access the uploaded data yourself using SQL queries. Here, for example, is a query to determine the effectiveness of campaigns from the Data Transfer-derived "Campaign" and "CampaignBasicStats" tables.