One of the biggest mistakes I see analysts make is jumping straight into BigQuery analysis without first verifying that their GA4 implementation is actually collecting reliable data.

The truth is simple:

Bad GA4 data becomes bad BigQuery data.

Before you spend hours building SQL queries, dashboards, or attribution models, it's worth taking a few minutes to validate your GA4 setup.

That's why I created a free checklist with 150+ GA4 implementation and data-quality checks.

Inside you'll find checks for:

✓ Events and conversions
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✓ Cross-domain measurement
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Whether you're an analyst, engineer, consultant, or marketer, this checklist can help you catch issues before they become expensive reporting problems.

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Thanks,
Anil Batra

Understanding event parameters is the key to unlocking the real power of GA4 data inside BigQuery.
Yet, for many marketers and analysts, event parameters remain confusing, messy, and hard to use.

That’s why I’m hosting a 1-hour live, hands-on class:

📌 Topic: GA4 Event Parameters in BigQuery
📅 Date/Time: September 24th at 10:00 AM PST
⏰ Duration: 1 Hour
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In this session, you’ll learn:

• How GA4 event parameters are structured in BigQuery

• Simple SQL queries to extract and flatten parameters

• Tips to transform raw data into insights you can present with confidence

By the end, you’ll know exactly how to make sense of event parameters — without getting lost in the nested data.

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Seats are limited, so don’t miss your chance to finally decode event parameters in BigQuery.

See you live!
Anil Batra, Founder Optizent Academy

The biggest challenge with learning anything new is the “getting started” part.

If you want to learn BigQuery but haven’t yet haven’t yet started then I recommend that you watch the following two videos:

• Getting Started with BigQuery for GA4: A Step by Step Guide

• How to Make Sense of GA4 Data in BigQuery

Best regards,
Anil Batra
Optizent Academy

P.S. Ready to take your learning to the next step? Check out this course: https://academy.optizent.com/courses/bigquery-for-marketers-and-marketing-analysts?coupon=BQNOW

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The course material includes the content I use to run full-day workshops at various conferences and organizations.

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• Learn and Master SQL: With simple, easy-to-understand lessons perfect for beginners and those who already know SQL. You will learn everything you need to know to use GA4 and other data sets.

• Master BigQuery: Learn how to work with BigQuery, Google’s powerful data warehouse, to run SQL queries on large datasets.

• Work with GA4 Data: Apply SQL and BigQuery to real-world GA4 data, helping you uncover deeper insights and make smarter business decisions.

• Step-by-Step Learning: No prior experience needed! We guide you through the entire process at a beginner’s pace.

🔑 Why this course is perfect for you:

• Build Essential Data Skills: SQL and BigQuery are critical tools for any data professional, and mastering them will set you apart in your career.

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Best regards,
Anil Batra
Optizent Academy

P.S. This is the only course that gets you from not knowing what BigQuery and SQL is to mastering them. Don’t miss this opportunity, click here to enroll, https://academy.optizent.com/courses/bigquery-for-marketers-and-marketing-analysts?coupon=BQBFOFFER

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• Master BigQuery: Learn how to work with BigQuery, Google’s powerful data warehouse, to run SQL queries on large datasets.

• Work with GA4 Data: Apply SQL and BigQuery to real-world GA4 data, helping you uncover deeper insights and make smarter business decisions.

• Step-by-Step Learning: No prior experience needed! We guide you through the entire process at a beginner’s pace.

🔑 Why this course is perfect for you:

• Build Essential Data Skills: SQL and BigQuery are critical tools for any data professional, and mastering them will set you apart in your career.

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• Huge Savings: 40% off with Black Friday offer.

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Best regards,
Anil Batra
Optizent Academy

P.S. This is the only course that gets you from not knowing what BigQuery and SQL is to mastering them. Don’t miss this opportunity, click here to enroll, https://academy.optizent.com/courses/bigquery-for-marketers-and-marketing-analysts?coupon=BQBFOFFER

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In this post I will show how you can use the GA4 data in BigQuery to build reports in Looker Studio.

To get started, make sure you have integrated GA4 and BigQuery and have a Looker Studio account ready.

Here are the steps

1. Go to Looker Studio and click on "Blank Report" to create a new report.

   

2. You will be promoted to "Add data to report".

3. Select BigQuery from the list of connectors. If you don't see BigQuery in the list then use the search bar above to search for BigQuery connector.

   

4. Pick the table that you want to use for your report.  You can only pick one table from the list. To add all 4 GA4 tables you will have to make 4 data connections. Let's first connect with daily events table.

   

5. Next pick the Configuration template, there is only one available for event table called "Event". The template provides you the data in a format that you can easily use in Looker Studio. I assume there will be more templates that will be released in the future.  For some of the tables, you might not see an option to pick a template. In that case you will just pick the table and go to Step 8.

6. Next click on the "Add" button in the top right bottom.

7. You might be promoted with a message confirming that you are adding a data to the report. Click on "ADD TO REPORT" button.

   

8. Now the data sources is added. You will see that Looker Studio automatically creates one table chart for you.

9. Go ahead and modify the looker studio any way you want. You will see all your dimensions and metrics available in the dimensions and metrics pickers.

10. Not the template that we picked in the step above will help you get the names of the paramater, counts of users etc.

11. To add additional tables from BigQuery (or any other data source), click on "Resource" menu option on top.

    

12. Select "Manage added data sources". This will show you all the data sources that you have added in your report.

13. Click on "+ ADD A DATA SOURCE" and you will be presented with the same screen that you saw in Step 6 above.

    

Now you are ready to build Looker Studio reports using GA4 data in BigQuery.

If you organization is data driven then the default template provided for tables is likely not going to be enough for your reporting needs.  In that case you will have to create databases views and table to meet your need. You will be able to connect with those tables and views the same way we did above.

If you need help with GA4, BigQuery or Looker Studio, reach out to us at support@optizent.com or  fill the contact us form.

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In the previous post, I walked you through the steps of enabling Gemini, a Google AI model, in BigQuery. In this of the post I will show you how to use AI in BigQuery.

After Gemini, Google’s AI model, is enabled in BigQuery, click on the “* Pencil” icon on the right hand side of console window. It will open up a dropdown with a list of all available Gemini features and a checkmark next to the ones that are enabled.

Clicking on the feature will allow you to disable it or enable it back again if you need it.

What Do These Features Do

Auto-Generation

With the features, you can write a natural language comment in the BigQuery query editor to generate a SQL query.

Here are the step to use this feature

• Type # (hashtag ) or -- (two dashes) to start the prompt.

• Next type your prompt

• Wait for Gemini to process your prompt and write the SQL

• Press tab to accept the SQL

In the screen below, is an example of a prompt and the SQL that Gemini wrote.

SQL generation tool

With this feature, you can enter natural language text in a tool to generate a SQL query, with options to refine query results, choose table sources, and compare results.

To use this feature, click on the *Pencil icon that appears in the query window (see the image below). You can also right click in the query window to open the SQL Generation tool.

Clicking on it will open the SQL Generation tool where you can write your prompt to generate SQL.

After you write your prompt and click “GENERATE” button, you will see the following window with SQL. You can then refine the SQL further or accept it as is. I will write more about this in the future posts.

Explanation

With this feature, you can prompt Gemini in to explain a SQL query in natural language. I will write more about this in the future posts. For now lets just see how it works.

Select SQL in the query window and right click on “Explain current selection”. This will open a panel explaining the SQL that you selected.

So that’s all for now. I hope Google AI in BigQuery helps you write better and fast SQL code. Click here to upgrade to support this newsletter and get advanced content.

Note: If you haven’t yet started with BigQuery then don’t be left behind. Here is a course to get you started, https://academy.optizent.com/courses/bigquery-sql-fundamentals-ga4?coupon=BQFUNDAMENTALSYT

If have some background in BigQuery (have been following this newsletter series) then checkout my BigQuery for Marketers and Marketing Analytics course. https://academy.optizent.com/courses/bigquery-for-marketers-and-marketing-analysts (Only BigQuery course designed for non-technical folks).

Need BigQuery and GA4 help?

Contact us at support@optizent.com or fill the contact us form.

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Gemini is a Google AI model that you can now use in BigQuery to assist you in writing SQL queries for any data including GA4.

It is a great tool for both beginners and experienced analysts. Beginners can learn SQL with help of Gemini while experienced folks can use it to expedite the SQL writing.

Gemini is not enabled by default in your BigQuery console. Follow the steps below to enable it.

Note: If you haven’t yet started with BigQuery then don’t be left behind. Here is a course to get you started, https://academy.optizent.com/courses/bigquery-sql-fundamentals-ga4?coupon=BQFUNDAMENTALSYT

How to Enable Gemini in BigQuery

1. Enable necessary APIs.

2. Grant required Identity and Access Management (IAM) roles.

3. Turn on Gemini in BigQuery features in the Google Cloud console.

Enable Gemini APIs

Note: You will need to be either an administrator or project owner with the serviceusage.services.enable permission to perform the following steps.

1. Go to Gemini for Google Cloud page.

2. Click Enable.

3. You will see the status as Enabled and now Gemini is available in BigQuery in the selected Google Cloud project.

IAM Permissions

1. Go to the IAM & Admin page.

2. Click on View by principals.

3. In the Principal column, find the user (principal) for which you want to enable access to Gemini in BigQuery

4. Click edit Edit principal in that row.

5. In the Edit access pane, click add Add another role.

6. In the Select a role list, select Gemini for Google Cloud User.

7. This will grant the Cloud AI Companion User IAM role (roles/cloudaicompanion.user) to the user.

Turn on BigQuery Features in Your Project

1. In the Google Cloud console, on the project selector page, select the Google Cloud project.

2. In the Google Cloud console, go to the BigQuery page.

3. In the toolbar, click the pen with sparks (Gemini) drop-down icon.
   

   

4. In the dropdown list of features, select the features that you want to enable in your project. I have enabled all the features (see the screenshot above):

   • Gemini in SQL query:

     • Auto-generation: Allows you to use Gemini in BigQuery to generate a SQL query using natural language prompts.

     • SQL generation tool: Same as above to generate a SQL query, with options to refine query results, choose table sources, and compare results.

     • Explanation: This help you use Gemini to explain the SQL query in natural language so that you can easily understand it.

   • Gemini in Python notebook:

     • Code completion (Preview): Use Gemini to contextually appropriate recommendations based on what you already have in the notebook.

     • Code generation: Use Gemini to generate Python code using natural language.

Now Gemini is enabled and ready to be used. In the next post, I will show how to use Gemini to generate SQL.

Need BigQuery and GA4 help?

Contact us at support@optizent.com or fill the contact us form.

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Are you ready to dive into the world of data analysis? 

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• Lifetime Access: Study at your convenience and revisit the material whenever you want.

🔑 Why this course is perfect for you:

• Build Essential Data Skills: SQL and BigQuery are critical tools for any data professional, and mastering them will set you apart in your career.

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If you have any questions or need more details, feel free to reply to this email. We can’t wait to help you on your learning journey!

Best regards,
Anil Batra
Optizent Academy

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Upgrade your BigQuery and SQL Skills

As a founding member of this BigQuery newsletter, you have the opportunity to lock in the premium version of the newsletter for a very low price. This price is about to go up, this might be your last chance to upgrade for a low price.

SQL and BigQuery skills will open a lot of new job opportunities in various fields such as Data Analytics, Data Engineering, and Marketing Analytics to name a few.

SQL is a skill that I learned 30 years ago to find my first job and it still pays me. 

The premium version provides:

1. Advanced lessons and content.

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If you have any questions then don’t hesitate to reach out to me at anil@optizent.com

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BigQuery Table Explorer provides a way for you to explore the data contained in various fields (columns) of a table and also create no-code SQL queries based on your selection of the fields.

This feature is great for both beginners who are learning SQL and getting familiar with BigQuery, SQL and the GA4 data, as well as for those who are experts and need to quickly review the distinct values in a a column.

Below are the step to use Table Explorer feature in BigQuery.

In the Google Cloud console, go to the project that contains the data that you want to explore.

In the Explorer pane, select the table that you want to explore and create the query for.

This opens up table schema. You can select the columns from the schema or click the “TABLE EXPLORER” tab, and then click “SELECT FIELDS” button.

Table Schema view

Table Explorer Tab

In the Select fields pane, you can select up to 10 table fields to explore.

Once you select the columns click on “EXPLORE” button on top (if you go from the Schema tab) or click “Save” button when you from “TABLE EXPLORER” tab.

This will now show you cards for each column that you selected and SQL Query based on your selection.

You can copy this query into a new query in query editor, or apply the query in table explorer.

Video of Table Explorer

If you want to watch the video of BigQuery Table Explorer then click on the following link.

https://youtu.be/Os9FQaalXNY

A few more things to know

1. Exceptions: Nested record type columns such as event_params are not supported in this feature.

2. Cost: Table explorer runs queries to display table exploration results. These queries incur compute pricing charges. You can also incur compute charges if you run a query generated by table explorer.

3. Permissions: You will BigQuery Job user on the project and BigQuery Data Viewer permissions on all the tables that you want to explore.

   If you created the account are admin then you have all the required permissions.

4. Learn BigQuery with GA4 data at https://academy.optizent.com/courses/bigquery-for-marketers-and-marketing-analysts

Happy Learning!

Anil Batra, Your Digital Analytics Coach
Optizent.com

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If you have already connected GA4 to BigQuery then you can skip this email. If you haven’t yet started then continue reading.

Recently a lot of Universal Analytics clients were scrambling to backup there old data. Some of them learned that they can't back up the entire historical data because of the data retention settings they had in place.

If you have migrated to GA4, it is time to make sure that you don't run into the same issues in the future.

In my recent live session, I covered three simple steps to get your GA4 data backed up in BigQuery every day, and that's what I am going to show you in this post. If you would rather watch the video then go to https://www.youtube.com/watch?v=n3vrKaMdtkw.

GA4 to BigQuery in 3 Simple Steps

1. Create a BigQuery Account and Project in Google Cloud

2. Enable GA4 to BigQuery Setting in GA4

3. Enable BigQuery API in Google Clouds

Step 1: Create a BigQuery and Project In Google Cloud

Go to https://cloud.google.com/bigquery

Click on "Get Started for Free" or a big button on the page that let's your create your account.

Go through the steps and make sure to enter your credit card and signup for paid version. Don't worry you won't be charged right away and you might never be charged as BigQuery give you a lot of free data storage and query processing every month. If you are worried then reach out to us and we can help you setup some alters which will alter you if you are about to go over your free threshold.

When you create an account, your project will be automatically created. Project is where your GA4 dataset will be created.

Step 2: Enable GA4 + BigQuery integration

Go to Admin panel in GA4.

Go to "Product links" section and click on "BigQuery links".

Follow the prompts and pick the project that you just created in the previous step.

Check all the boxes that you see. If you want to know what those boxes are for then watch the video.

Go ahead and save.

Now you are ready to send the GA4 data to BigQuery.

Step 3: Enable BigQuery APIs.

In BigQuery click on the hamburger menu in the top left corner, scroll down to API & Services and then select Library.

You can also go directly to https://console.cloud.google.com/apis/library

Search for BigQuery APIs and then click on enable button.

That's it, now your GA4 data will start to flow to BigQuery. You will have to wait for 24 hours or so for the first day of the data.

We can get you and your team trained on BigQuery. If interested then email me at support@optizent.com. We can also help you setup everything and get it working for you.

Watch The GA4 to BigQuery in 3 Steps Video

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Upgrade by visiting https://bigquery.beehiiv.com/upgrade

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String functions in BigQuery allow you to manipulate the data that you retrieve with your SQL queries.

In this post, I am introducing 10+ string functions that you must know to excel in BigQuery. In future posts, I will share specific use cases for these functions.

1. CONCACT(value 1, value 2, value 3, ….) - This function allows you to combine various values into a single value. For example, if you have first name and last name in separate fields but you need the full name of the person then you can use this function to combine those values.

   Example: SELECT CONCAT(“Anil”, “ “, “Batra”) will return ‘Anil Batra’

2. LOWER(value)- This function turns all the characters of the specified value into lowercase.

   Example: SELECT LOWER(“OPTIZENT”) will return ‘optizent’

3. UPPER(value) - This function turns all the characters of the specified value into uppercase.

   Example: SELECT UPPER(“optizent’) will return ‘OPTIZENT’

4. CHAR_LENGTH(value) - This function returns the number of characters in the specified value.

   Example: SELECT CHAR_LENGTH(“OPTIZENT”) will return 8

5. CHARCATER_LENGTH(value) - Similar to CHAR_LENGTH, this function returns the number of characters in the specified value.

   Example: SELECT CHARACTER_LENGTH(“OPTIZENT”) will return 8

6. LENGTH(value) - Similar to CHAR_LENGTH, this function returns the number of characters in the specified value.

7. SUBSTR(value, position, [length]) - this function returns a portion of the specified value starting from the position that is specified in the function to the end of the string or number of characters specified in the length parameter, which is optional.

   Example: SELECT SUBSTRING(“Anil Batra”, 6) will return ‘Batra’

   SELECT SUBSTRING(“Anil Batra”, 6, 3) will return ‘Bat’

8. SUBSTRING(value, position, [length])) - This is an alias of SUBSTR and works just like it.

9. SPLIT(value, [delimiter]) - This function splits the specified value, containing multiple values separated by a delimiter, into an array of individual values. The delimiter is optional and the default delimiter is a comma.

   Example: SELECT SPLIT(“Anil, Batra, Optizent”) will return an array as [“Anil”, “Batra”, “Optizent”]

10. TRIM(value, [set of characters to be removed]) - This function removes the specified set of characters from the beginning and end of the value passed. The set of characters is optional and if nothing is specified then whitespace characters are removed. The values passed in the function are case-sensitive.

    Example: SELECT TRIM(“ Anil Batra ”) will return “Anil Batra”

    SELECT TRIM(“ Anil Batra”, "a”) will return “Anil Batr”

11. RTRIM(value, [set of characters to be removed]) - Similar to TRIM but only the ending character (right-hand side) is removed.

    Example: SELECT RTRIM(“ Anil Batra”, "A”) will return “Anil Batra” since there is no lowercase “a” on the right-hand side

12. LTRIM(value, [set of characters to be removed]) - Similar to TRIM but only the leading character (left-hand side) is removed.

    Example: SELECT LTRIM(“ Anil Batra”, "A”) will return “nil Batra”.

13. INITCAP(value, [delimiters]) - return the value by capitalizing the initial character of each value separated by the specified delimiters. If no delimiter is specified then whitespace is considered the delimiter.

    Example: SELECT INITCAP(“anil batra”) will return “Anil Batra”

14. LEFT(value, length) - Get a specified number of characters starting from the left of the value specified in the function.

    Example: SELECT LEFT("Optizent Analytics", 8 ) will return Optizent.

15. RIGHT(value, length) - Get the specified number of characters starting from the right of the value specified in the function.

    Example: SELECT RIGHT("Optizent Analytics", 9 ) will return Analytics.

16. LPAD(value, return length, [pattern to use for padding]) - This function returns the number of characters specified in return length by adding the pattern to the specified value.

    Example: SELECT LPAD("Optizent Analytics", 20, "@") will return “@@Optizent Analytics”. Since the length of the input value (Optizent Analytics) is 18, the function added two @ symbols to make it a length of 20.

17. RPAD(value, return length, [pattern to use for padding]) - Same as LPAD but adds the pattern at the end.

    Example: SELECT RPAD("Optizent Analytics", 20, "@") will return “Optizent Analytics@@”. Since the length of the input value (Optizent Analytics) is 18, the function added two @ symbols to make it a length of 20.

In the above examples, I took a static string value to illustrate how these functions work but you can use these functions on any column in SQL commands.

Stay tuned for many such lessons.

Thank you,
Anil Batra, Optizent.com

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A group of users who share a common characteristic is called a Cohort. For example, you can create a cohort of all the users who were acquired on a particular date or a cohort of all the users who visited your site on a particular date/week/month, etc. In GA4, you can build a cohort exploration to understand the behavior of a cohort.

Here is a cohort report showing you the users who did any activity on a particular date (in rows). In the columns, you shave the retention of those users.

For example, on May 13th (first row of data) there were 629 users who did any activity on the site. I used “any event” as cohort criteria and “any event” as return criteria to see the activity. Of those 629, only 37 came back the following day, and only 17 the following day, and so on.

If you are not familiar with how this exploration works then join my GA4 Newsletter, I will discuss it there. In this post, I will share how you can create similar reports using BigQuery.

In GA4 there are a few things you have to do.

1. Decide the granularity of the data. In my example, I am using it daily.

2. Get the list of users who belong to the cohort. In my example, I am looking at users on a particular day, who did any activity on the site.

3. Use that list to query if the user returned on the following day.

4. Repeat this for the date range you want to build the report for.

5. Repeat the above 4 steps for each of the dates in different rows.

Check out the past editions of this newsletter to see how I write the various queries. I am using the concepts that I have covered in the past.

Sample Query

SELECT distinct user_pseudo_id 
FROM `optizent-test-project.analytics_XXXXX.events_20240413` 
where is_active_user = true

The above query gives you a list of active users on a given date, 13th May in the example above.

select count(distinct user_pseudo_id) 
FROM `optizent-test-project.analytics_289314775.events_202405*` 
where is_active_user = true
and user_pseudo_id in ('the users selected in the first query')
and _TABLE_SUFFIX BETWEEN '14' and '19'

This query gives you the count of users who came on 13and and also came on the 6 days following the main date.

Use the sample queries to practice how to use GA4 data in BigQuery. Stay tuned for more such queries and use cases

Thank you,
Anil Batra, Optizent.com

In the past several posts, I have shown you how to recreate various GA4 standard metrics in BigQuery.

In this post, I am going to bring all of these together so that you can have all of these in one post. This is the post that you might want to bookmark.

Views:

SELECT  count(*) 
FROM  `your-project.your-dataset.events_######`
WHERE event_name = 'page_view'

In addition to the Views metrics, GA4 also has “Views per session” and “Views per user” metrics.

More details at https://bigquery.optizent.com/p/page-view-related-metrics-ga4-bigquery

Sessions

There are three ways to get the “Session” metrics in GA4 and each one tells a different story. Here is the right one

SELECT COUNT(DISTINCT CONCAT(user_pseudo_id,value.int_value))
FROM your_project_id.your_dataset_id.your_table_id, unnest(event_params)
WHERE key = 'ga_session_id'

More details at https://bigquery.optizent.com/p/calculate-ga4-sessions-in-bigquery

Engaged Sessions

Here is the SQL. Check the details at https://bigquery.optizent.com/p/ga4-engaged-sessions-bigquery

SELECT
 COUNT(DISTINCT
 CASE
     WHEN (SELECT value.string_value FROM      UNNEST(event_params) WHERE key = 'session_engaged') = '1'
  THEN CONCAT(user_pseudo_id,(select value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')) 
  END) as engaged_sessions
from
  `your-project.your-dataset.events_######`

Total Users

“Total Users” is the total number of people who visited your site or app in the specified date range. As mentioned above we will use two different methods to calculate the “Total Users” metrics.

1. Using user_pseudo_id column in GA4, the SQL will be:

SELECT COUNT(DISTINCT user_pseudo_id)
FROM your_project_id.your_dataset_id.event_######

Since each user’s ID is stored in the user_pseudo_id column, we just need to a distinct count (unique count) of those IDs.

2. Using user_id column in GA4, the SQL will be

SELECT COUNT(DISTINCT user_id)
FROM your_project_id.your_dataset_id.event_######

In the above SQL, instead of using user_pseudo_id, I used the user_id column, which stores the known user IDs.

To calculate Active Users check, https://bigquery.optizent.com/p/calculate-total-users-and-active-users-ga4-bigquery

Are you enjoying this newsletter? Would you be willing to write a testimonial?
If yes then please hit reply with your comments.

Thank you,
Anil Batra, Optizent.com

P.S. Need help with GA4 BigQuery? Email me at anil@optizent.com

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