When you manage a large number of websites and all your business decisions are based on the statistics of your website-related data then 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 gain information 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.
Google Search Console is a Google web service that allows webmasters to check indexing status, crawling errors, search queries, and optimize website visibility. You can also use it to ensure that any site maintenance or changes you make have a positive impact on search performance.
You can load your web and search-based data from Google Search Console to BigQuery by implementing the integration of both platforms. In this article, we have described two methods to achieve this:
Method 1: Connecting Google Search Console to BigQuery using Hevo’s No-Code Data Pipeline
Hevo Data, a fully managed No-Code Data Pipeline will help you set up a GCS BigQuery connection without writing any code. You can utilize its plug-and-play platform to set the data moving in a few minutes with 100% accuracy and zero data loss.
With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 150+ Data Sources (including 50+ free sources) straight into your Data Warehouse or any Databases.
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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.
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 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.
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.
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:
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:
- Configure Destination: To configure Google BigQuery as a destination, fill in the following fields:
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:
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- Minimal Setup: You will require minimal setup and bandwidth to set up a Google Search Console BigQuery connection using the Hevo platform.
- No Data Loss: Hevo architecture is fault-tolerant and allows easy, reliable, and seamless transfer of data from Google Search Console to BigQuery without data loss.
- 100’s of 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.
- Exceptional Support: Hevo has 24×7 Technical support through emails, calls, and chat.
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.
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.
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.