How to Utilise ChatGPT For BigQuery

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Utilise the power of AI chatbots like ChatGPT to run intricate BigQuery queries and enhance your SEO reporting capabilities. Learn how to streamline tasks, make strategic decisions, and reduce manual workload by leveraging AI in your data analysis process.

Key Takeaways:

  • AI Operator Assistance: Utilising ChatGPT for BigQuery can help in running complex queries for SEO reporting, allowing more focus on strategic decision-making.
  • Effective Data Analysis: ChatGPT can streamline the process of combining data from Google Search Console and Google Analytics 4 to gain valuable insights into search traffic and engagement metrics.
  • Human Oversight: While AI chatbots like ChatGPT can assist in coding and generating queries, human operators are imperative to provide context, guidance, and to ensure the accuracy of the results produced.

Why Do You Need to Learn BigQuery?

Limitations of SEO Tools

Need to understand that SEO tools like Google Search Console or Google Analytics 4 have their limitations. For instance, Google Search Console often omits anonymized queries and imposes restrictions on the number of rows you can export. This data sampling issue can hinder comprehensive analysis, especially for larger websites.

Benefits of Using BigQuery

One of the key advantages of using BigQuery is the ability to run complex reports without encountering data sampling limitations. By utilising BigQuery, you can access and analyse all your data, allowing for more detailed insights and strategic decision-making in your SEO reporting.

Plus, the advanced SQL capabilities of BigQuery enable you to perform in-depth analysis and combine data from various sources, offering a more comprehensive view of your SEO performance. With the scalability and efficiency of BigQuery, you can handle large datasets and conduct detailed analyses, bringing significant benefits to your SEO strategies.

SQL Basics

Introduction to SQL Statements

Statements in SQL are fundamental commands used to interact with databases. These commands include SELECT for retrieving data, INSERT for adding new data, and DELETE for removing data. If you want to learn how to create prompts similar to using ChatGPT in BigQuery, check out How to do a text ChatGPT-like text prompt in BigQuery.

Conditions in SQL

An crucial aspect of SQL is the use of conditions to filter and manipulate data. Common conditions like WHERE, AND, OR, and LIKE help in specifying criteria for selecting or modifying records in a database.

Another vital condition in SQL is EXISTS, which checks if a subquery returns any records. Understanding and mastering various conditions in SQL is crucial for creating accurate and efficient database queries.

Preparing to Use ChatGPT with BigQuery

Connecting GSC and GA4 Accounts to BigQuery

To start utilising ChatGPT with BigQuery, the initial step is to connect your Google Search Console (GSC) and Google Analytics 4 (GA4) accounts to BigQuery. This connection allows you to access and analyse the data from these platforms more comprehensively.

Understanding BigQuery Project and Dataset Structure

The foundation for effectively using ChatGPT with BigQuery lies in understanding the structure of BigQuery projects and datasets. This comprehension is vital for crafting precise queries that extract the desired data for in-depth analysis.

It is important to grasp how projects and datasets are organised within BigQuery as it dictates the way data is accessed and queried. This knowledge ensures that queries are accurate and efficient, leading to successful data analysis and reporting.

Example 1: Analyzing Traffic Decline Due to Google Algorithm Impact

For Supercharge your data analysis with BigQuery and ChatGPT!, let’s examine into analysing traffic decline caused by a Google algorithm update.

Running Reports on Affected Pages

Google Search Console (GSC) limits exporting data to 1,000 rows, hindering analysis for websites with numerous pages. By utilising BigQuery, you can compare data before and after the algorithm change, providing insights into the impact.

Using ChatGPT to Generate SQL Queries

Any data analyst well-versed in Google Analytics and SQL can leverage ChatGPT to craft intricate SQL queries seamlessly. By specifying requirements and dataset details, you can extract specific insights from your data.

Traffic decline necessitates in-depth analysis, requiring precise SQL queries to uncover trends and anomalies post-algorithm updates.

Fixing Errors and Refining the Query

Using BigQuery and ChatGPT, you can quickly rectify syntax errors in generated SQL code by cross-referencing table schemas. This ensures accurate data retrieval and smooth query execution.

A keen attention to detail during SQL query generation guarantees precise and insightful reporting, enhancing decision-making processes in SEO analysis.

Example 2: Combining Search Traffic Data with Engagement Metrics from GA4

Understanding GA4 Table Structure

Structure your understanding of GA4 tables by noting that it includes an event_params column with dimensions like page_location, ga_session_id, and engagement_time_msec. This column tracks user engagement by measuring the time spent on specific interactions on your website, summing up those moments to calculate average engagement time per session.

Using ChatGPT to Generate SQL Queries

For generating SQL queries using ChatGPT for GA4 data, provide context and information about how GA4 calculates the engagement_time_msec metric from its official documentation. Supplying this additional detail aids ChatGPT in composing accurate queries that combine GA4’s engagement metrics with data from Google Search Console.

Joining GA4 and GSC Data by URLs

Table your approach to joining GA4 and GSC data by URLs, combining engagement metrics from GA4 with performance data from Google Search Console. This process enables a comprehensive analysis that correlates search traffic data with user engagement metrics, providing valuable insights into the relationship between rankings and user interaction.

Tips and Considerations

Many insights can be gained by utilising ChatGPT for BigQuery to enhance your SEO reporting. Here are some tips to consider:

  • Ensure to provide detailed context and guidance when requesting queries from ChatGPT to improve accuracy.
  • Regularly cross-reference data from different sources, such as GA4 and Google Search Console, for a comprehensive analysis.
  • Perceiving potential discrepancies between GA4 UI and BigQuery data is crucial for understanding data variations.

Handling Large Datasets

To efficiently handle large datasets in BigQuery, it is important to optimise your queries for scalability. You can save results as BigQuery tables and utilise tools like Looker Studio for data visualisation. For a detailed tutorial on composing queries for GA4 in BigQuery using ChatGPT, check out the GA4 BigQuery Composer Tutorial for ChatGPT.

Understanding Discrepancies between GA4 UI and BigQuery Data

To address differences between data in GA4 UI and what is queried from BigQuery, focus on queries that include active users to align results more closely with GA4 UI. Furthermore, understanding the sampling methodologies and data modelling in GA4 can help in interpreting variations between the two data sources.


Considering all points discussed in this guide, it is evident that leveraging AI chatbots like ChatGPT for executing complex BigQuery queries can significantly enhance the efficiency of SEO reporting processes. By utilising ChatGPT’s capabilities to automate data analysis tasks, SEO experts can focus more on strategic decision-making rather than being bogged down by manual processes. The examples provided illustrate the effectiveness of incorporating AI technology in streamlining data analysis and reporting, ultimately improving overall decision-making in SEO.


Q: How can I utilise ChatGPT for BigQuery?

A: To utilise ChatGPT for BigQuery, you can follow the step-by-step guide provided in the article. ChatGPT can help you run complex BigQuery queries for SEO reporting, allowing you to focus on strategic decision-making.

Q: Why is learning BigQuery important for SEO reporting?

A: Learning BigQuery is important for SEO reporting because tools like Google Search Console and Google Analytics 4 have limitations in terms of data sampling. By using BigQuery, you can access complete data sets, run complex reports, and eliminate the issue of data sampling.

Q: How can AI chatbots like ChatGPT enhance data analysis for SEO purposes?

A: AI chatbots like ChatGPT can streamline data analysis tasks by composing complex BigQuery queries. They can combine data from different sources, such as Google Search Console and Google Analytics 4, providing deeper insights for SEO analysis. By using ChatGPT, you can efficiently blend data and focus on making informed strategic decisions.

Tags: BigQuery, ChatGPT, Utilise

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