When you manage a large number of websites and all your business decisions are based on the statistics of your website-related data, it can get difficult to process, manage, and store the exponentially increasing data coming from Google Search Console. This is when there is a need to store all your website-related statistical data in a Single Source of Truth (SSOT), i.e., a data warehouse. However, traditional on-premises databases are inefficient and resource-intensive when it comes to handling ever-growing data.

This is when an economical and stable solution would be opting for a Cloud Data Warehouse solution like Google BigQuery. In this article, you will learn about connecting Google Search Console to BigQuery. You will also gain a holistic understanding of Google Search Console, and Google BigQuery, their key features, and the need for migrating your data from Google Search Console to BigQuery.

What is Google Search Console?

Google Search Console is a one-stop solution for visualizing the performance of your website on Google. It includes a variety of tools and reports that provide a comprehensive picture of how your site is performing, such as performance reports, mobile usability, core web vitals, etc.

You can use this console to submit sitemaps to Google, speed up crawling, and prevent certain parts of your site from appearing in search results.

If you operate a website and want to evaluate and increase your organic traffic from Google searches, you must have a search console account. 

Google Search Console shows you how Google sees, crawls, and indexes your website. It helps website owners analyze and optimize their websites to rank higher in Google search results.

It also informs them of any problems with the website or indexing that Google crawlers may disregard.

Key Features of Google Search Console

Some of the key features of Google Search Console are as follows:

  • Mobile-first indexing: Users can only monitor mobile usability, which helps them learn about mobile traffic on websites.
  • Keyword Analysis: Google Search Console enables you to search for and analyze keywords for which your website ranks.
  • URL Inspection: Google Search Console enables you to inspect any specific URL in Google’s index and compare it to the page as it appears on your website.
  • Reporting and Dashboard: Google Search Console includes personalized and customizable dashboards and reports to help you analyze data more efficiently.

To learn more about Google Search Console, you can visit here.

What is Google BigQuery?

Google BigQuery is a Cloud-based Data Warehouse that offers a Big Data Analytic Web Service that can process petabytes of data. It is designed for large-scale data analysis. It is divided into two parts: storage and query processing.

It uses the Dremel Query Engine to process queries and stores them on the Colossus File System. These two components are decoupled and can be scaled separately and on demand.

BigQuery employs Columnar Storage for fast data scanning, as well as a tree architecture for executing ANSI SQL queries and aggregating results across massive computer clusters.

Furthermore, Google BigQuery is serverless and designed to be extremely scalable due to its short deployment cycle and on-demand pricing.

BigQuery’s scalable, distributed analytical engine allows you to query terabytes and hundreds of petabytes of data in seconds. BigQuery is an “externalized version” of Google’s Dremel query service software, which was released as V2 in 2011.

BigQuery adds flexibility by decoupling the computational engine that analyses your data from your storage options. BigQuery can be used to store and analyze data, or it can be used to review data stored elsewhere.

For further information about Google Bigquery, you can follow the Official Documentation

Key Features of Google BigQuery

Google Search Console to BigQuery: Architecture of Google BigQuery | Hevo Data
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Google BigQuery has continuously evolved over the years and is offering some of the most intuitive features:

  • User-Friendly: You can store and analyze your data in Big Query with just a few clicks. Since, you don’t need to deploy clusters, set your storage size, or configure compression and encryption settings, an easy-to-use interface with simple instructions allows you to quickly set up your cloud data warehouse.
  • Scaling of On-Demand Storage: With ever-increasing data demands, you can easily ensure that it will scale automatically as and when needed. Based on Colossus (Google Global Storage System), BigQuery can store data in a columnar format with the ability to work directly on the compressed data without having to decompress the files on the go.
  • Real-Time Analytics: BigQuery will help you stay updated with real-time data transfer and accelerated analytics. It accordingly allots resources to provide the best performance and results, allowing you to generate business reports as needed.
  • Scalability: Using its massively parallel computing and secure storage engine, Google BigQuery provides true scalability and consistent performance.
  • Data Ingestion Formats: Google BigQuery allows you to load data in a variety of formats, including AVRO, CSV, JSON, etc.
  • Parallel Processing: It employs a cloud-based parallel query processing engine to read data from thousands of discs at the same time.
  • Secure: BigQuery administrators can set data access permissions for groups and individuals. Row-level security can also be enabled to restrict access to specific rows of a dataset. Data is encrypted both before it is written to the disc and during the transit phase. It also allows you to manage your data’s encryption keys.

For further information on Google BigQuery, you can check the official website here.

Methods to Connect Google Search Console to BigQuery

You can set up a Google Search Console BigQuery connection in 2 ways. Those are as follows:

Explore these ways to Connect Google Search Console to BigQuery

Two methods to achieve Google Search Console to BigQuery are:

Method 1: Connecting Google Search Console to BigQuery using Hevo’s No-Code Data Pipeline

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. You can utilize its plug-and-play platform to set the data moving from 150+ Data Sources (including 50+ free sources) in a few minutes with 100% accuracy and zero data loss.


Method 2: Connecting Google Search Console to BigQuery Using the REST APIs

In this method of connecting Google Search Console to BigQuery, you can use any of the 2 REST APIs to extract data from Google Search Console, transform it and then load it into Google BigQuery.

Both methods are explained below.

Method 1: Connecting Google Search Console to BigQuery using Hevo Data

Hevo Data helps you directly transfer data from Google Search Console to BigQuery in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.

Hevo takes care of all your data preprocessing needs required to set up a connection from Google Search Console to BigQuery and lets you focus on key business activities.

Follow Hevo’s guide for connecting Google Search Console to BigQuery using No-Code Data Pipeline:

Google Search Console to BigQuery: Configuring Google Search Console as a Source | Hevo Data
  • Configure Destination: To configure Google BigQuery as a destination, fill in the following fields:
Google Search Console to BigQuery: Configuring Google BigQuery as a Destination | Hevo Data

And voila! You can start replicating data from Google Search Console to BigQuery with the pipeline you just built.

Advantages of using the Hevo Data Platform:

  • More than 150 Out of the Box Integrations: Hevo platform brings data from other sources such as SDKs, Cloud Applications, Databases, and so on into Data Warehouses and Databases. So, Hevo is the right partner for all your growing data needs.
  • Automatic Schema Detection and mapping: The schema of incoming data is scanned automatically.  If there are changes detected, they are handled seamlessly and the changes are incorporated into the Database or Data Warehouse.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
Sign up here for a 14-Day Free Trial!

Method 2: Connecting Google Search Console to BigQuery using REST API

The steps involved in connecting Google Search Console to BigQuery using REST API are as follows:

Step 1: Access Your Data On Google Search Console

  • You can access your data from Google Search Console through the Search Console APIs. The 2 APIs available are:
    • Search Console API
    • URL Testing Tools API
  • If the Search Console API is taken into consideration, then in that case you first need to authorize yourself by implementing the OAuth 2.0 protocol to get access to the API.
  • The things that should be kept in mind while dealing with the Google Search Console API are:
    • Rate Limits: You have to take into account the rate limits associated with the Search Console API.
    • Authentication: You need to authenticate on Google using an OAuth.
    • Paging and dealing with a big amount of data: Google and some other platforms generate a lot of data. Hence, pulling a massive amount of data from an API might be difficult.

Step 2: Transform And Prepare Your Google Search Console Data

After accessing your data on Google Search Console, you have to transform it based on 2 two primary factors. They are as follows:

  • Limitations of the database where the data will be loaded into.
  • Type of analysis that you are going to perform.

Each system has specific limitations associated with it such as the data types and data structures supported by the system. 

For instance, as in this case, you want to load data from Google Search Console to BigQuery then:

  • You can send nested data like JSON directly.
  • If you’re dealing with tabular data stores, you have to flatten out your data before loading it into the database.
  • You have to select the appropriate data types. And also, depending on the system to which you will send your data and the data types exposed by the API, you must make the appropriate choices. 
  • Finally, you must map one report to a table in your database and ensure that all data is stored in it. Dimensions and metrics will be transformed into columns of data.
  • To avoid duplicates, you must take special care that the reports you receive from Google Search Console do not contain the primary keys provided by Google.

Step 3: Load Data From Google Search Console To Google BigQuery

Since you want to load Google Search Console data to Google BigQuery, you can use one of the following supported methods:

  • Google Cloud Storage
  • Sent data directly to BigQuery with a POST request
  • Google Cloud Datastore Backup
  • Streaming insert
  • App Engine log files
  • Cloud Storage logs

From the above list of sources, App Engine log files and Cloud Storage logs are not applicable in this case.

Limitations of Connecting Google Search Console API to BigQuery using REST API:

  • Complexity: The design of REST API is complex as compared to other APIs as it uses architectural principles for its functioning. This can appear to be complex for you if you are not familiar with it.
  • Less Flexible: Due to its architectural complexity, REST APIs are less flexible. Also, they have lower performance than other APIs.
  • Requirement of Coding Expertise: To use REST API in order to send data from Google search console to BigQuery, one is required to write and maintain a script. This requires sound knowledge of coding and programming. 

How To Get Your GSC Data In BigQuery Using Bulk Data Export

1. Create A Google Cloud Project With BigQuery And Billing Enabled

  • You can create a project in Google Cloud with BigQuery by enabling billing.
  • To access the Console, click on the project you currently are in on the top left, (or Select a project if you have none). A popup will be opened.
  • Click on NEW PROJECT and follow the further steps. You should be careful when you choose the region as you will have to pick the same region when you set up the bulk export in the GSC. For example, to query two datasets such as GSC and GA4 data, they should be in the same region.

2. Setting up the Bulk Data Export In the GSC Property

  • Now the bulk data export to your new Google Cloud project can be activated directly in the Google Search Console.
  • In the Settings section of the property you want to export data from, click on Bulk data export.
  • You can paste the Cloud project ID here. You need to pick the same dataset location that you chose for your Google Cloud project.
  • Now click on Continue. The GSC will inform you about the functionality of the initial setup. The dataset will also be created in your project and data export will start in 48 hours. 

Limitations of Connecting Google Search Console to BigQuery using Bulk Data Export

  • Data Availability: Bulk Data Export only includes data from the past 90 days, preventing access to historical performance data beyond that timeframe
  • Limited Data Fields: While covering core metrics like clicks, impressions, and CTR, the export omits some specific data points available in the GSC interface.

Use Cases of Migration of Google Search Console Data to BigQuery

  • Geospatial Analytics: BigQuery’s Geographical Information System (GIS) enables you to analyze and visualize geospatial data. Thus, Search Console to BigQuery migration can help in effective geospatial analytics. 
  • Optimized Data Analysis: BigQuery’s features such as Partitioned Tables, Materialized Views, and BI Engine, help optimize query performance. Hence, when you analyze Google search console data in BigQuery, it helps to make an efficient analysis of your data. 
  • Business Intelligence: After your Google Search Console BigQuery data migration,  BigQuery assists in making interactive dashboards and reports which may help business enterprises to make data-driven decisions.

Why Sync Data from Google Search Console to BigQuery?

  • Your Google Search Console account contains a lot of information about how your website appears and performs in search results.
  • It provides a plethora of statistics such as search visibility, search traffic, technical status updates, crawl data, and much more.
  • Marketers can combine GSC data with other apps and tools in BigQuery to analyze data from multiple channels simultaneously and generate reports quickly.
  • Data loaded from Google Search Console to BigQuery enables site administrators and marketers to make accurate, informed decisions about their website’s search visibility efforts.
  • Furthermore, you can not only automate internal processes but also uncover insights that can help you make better decisions, optimize processes, and serve customers more effectively.
  • It requires extensive programming and knowledge of frameworks for Machine learning on large datasets. BigQuery ML can be used to increase development capabilities and speed with simple SQL.
  • You can use the BigQuery data security and governance features, to include your search data on BigQuery. Thus, you will not need separate rules for separate products.
  • Almost Sixteen months of data can be stored in the Search Console. You can store as much data as you want by using BigQuery. By default, data remains permanently in your BigQuery dataset. You can update the default partition expiration times to limit your storage costs.
  • By using Google search console BigQuery best practices, in addition to looking at the average Click Through Rate (CTR), you can also calculate the CTR for each individual page and search query. Further, if you manage a brand with more than one website, this allows you to look at clicks across all these sites at once.


In this article, you learned about connecting Google Search Console to BigQuery. This article also focused on Google Search Console, Google BigQuery, their key features, and the need for migrating your data from Google Search Console to BigQuery. Also, after providing information on how to send data from Google search console to bigquery this article includes its real life use cases.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

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Hevo Data with its strong integration with 150+ Data Sources (including 50+ Free Sources) such as Google Search Console allows you to not only export data from your desired data sources & load it to the destination of your choice such as Google BigQuery but also transform & enrich your data to make it analysis-ready. Hevo also allows the integration of data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price, which will assist you in selecting the best plan for your requirements.

Share your experience of understanding connecting Google Search Console to BigQuery in the comment section below! We would love to hear your thoughts.

Manisha Jena
Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.

No-code Data Pipeline for Google Search Console &Google BigQuery