Connect Google Search Console to BigQuery in Minutes

google search console bigquery integrate

Analyze Google Search Console data with BigQuery


Why Move Data From GSC to BigQuery?

Here are some reasons why organizations might choose to transfer their data from Google Search Console to BigQuery:


1. Advanced Data Analysis

While Google Search Console offers basic reporting and insights into website performance in search results, BigQuery provides a more powerful and flexible analytics platform. By migrating Search Console data to BigQuery, you can perform complex queries and analysis on your search data. This enables you to gain deeper insights into search traffic patterns, keyword performance, click-through rates, and other valuable metrics that can help optimize your website’s visibility and performance.


2. Data Integration and Consolidation

BigQuery allows you to combine data from multiple sources, including Google Search Console, with other data sets. This integration capability enables a more comprehensive analysis by correlating search data with other business data. By consolidating data in BigQuery, you can gain a holistic view of your online presence and make data-driven decisions that span different aspects of your business.

By moving your Search Console data to BigQuery, you can store and retain your historical data for extended periods. This extended data storage allows you to conduct trend analysis, perform year-over-year comparisons, and analyze long-term patterns in your website’s performance.


3. Scalability and Performance

Google Search Console provides a limited set of predefined reports and data export options. BigQuery, on the other hand, offers virtually unlimited scalability and processing power. It can handle large volumes of data and perform queries quickly and efficiently, even with complex analysis requirements. This scalability ensures that your analysis is not restricted by data size or query complexity and allows for faster iterations and insights.


4. Machine Learning and Predictive Analytics

BigQuery integrates with Google Cloud’s machine learning services, such as BigQuery ML and AI Platform. By combining your Search Console data with other datasets and leveraging these machine learning capabilities, you can build predictive models, identify trends, and generate insights that help optimize your website’s SEO strategies and drive better business outcomes.



Connect Google Search Console to BigQuery

It is very simple to connect Google Search Console to Google BigQuery, it can be done in a fast and easy manner with

Before you start, please, make sure that you have admin access to a BigQuery instance and that you have access to Google Search Console property.

So once you have that, head to onboard.


First Step:

You need to select Google Search Console as a Data Source and click “Grant Search Console Access”, then you will prompted to authenticate and then redirected back here. And then your Google Search Console properties will appear.

search console connector


Second Step:

Once you select the data source, click the “Next (Data Preview Button)” or “Preview and Destination”

So now it is time to select the fields or the metrics and dimensions you would like to have available in BigQuery.

Select BigQuery by clicking on the logo, as shown in the screenshot below.  

select bigquery


Third Step:

Once you select Bigquery, click the Add Destination Task Button and fill out necessary fields . 

create bigQuery destination task

Fourth Step:

In the final step, grant access to the user: That’s all!

Once you go through these steps, you will see that the data is automatically populated into your BigQuery account.


Note: As a connector URL, you can use any URL providing a JSON. Either from the connectors or for example a URL with cached and transformed data.


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Connecting Google Search Console to BigQuery using REST API


Step 1: Retrieve Your Data from Google Search Console

Accessing data from Google Search Console can be achieved through the Search Console APIs.

There are two available APIs: the Search Console API and the URL Testing Tools API.

If utilizing the Search Console API, initial authorization is required through the implementation of the OAuth 2.0 protocol.

Key considerations when working with the Search Console API include being mindful of rate limits, authentication through OAuth, and addressing the challenges associated with handling large volumes of data.


Step 2: Transform and Prepare Google Search Console Data

After obtaining data from Google Search Console, the next step involves transforming it based on two primary factors:

  1. Database Limitations: Consider the limitations of the destination database where the data will be loaded, including supported data types and structures.
  2. Analysis Type: Consider the type of analysis you intend to perform on the data.


When loading data from Google Search Console to BigQuery, specific considerations include:

  • Sending nested data like JSON directly.
  • Flattening tabular data before loading it into the database.
  • Selecting appropriate data types based on the destination system and API-exposed data types.
  • Mapping reports to tables in the database, ensuring proper storage of all data, and transforming dimensions and metrics into data columns.


To prevent duplicates, special attention must be given to ensuring that reports received from Google Search Console do not contain primary keys provided by Google.


Step 3: Load Data from Google Search Console to Google BigQuery

To load Google Search Console data into Google BigQuery, you can choose from several supported methods, including:

  1. Google Cloud Storage
  2. Directly sending data to BigQuery with a POST request
  3. Google Cloud Datastore Backup
  4. Streaming insert


Try today

Access all your data from your favorite sources in one place.
Get started for free with a 30 - day trial.

Start Free Trial

Google Search Console metrics & dimensions available for streaming into BigQuery

Extract Google Search Console data to BigQuery with

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Providing 70+ marketing data streams, we make sure that all the data we integrate is fresh and accessible by marketers, whenever they want.


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