GA4 BigQuery Newsletter For Marketing Analytics

💡 BigQuery Tip of the Week: Use QUALIFY to find the latest event for each user

Anil Batra — Thu, 10 Sep 2026 22:31:05 +0000

Need the most recent event for every GA4 user?

You could create a subquery with ROW_NUMBER() and then filter it. But BigQuery gives you a cleaner option: QUALIFY.

SELECT
  user_pseudo_id,
  event_name,
  event_timestamp
FROM `project.analytics_123456789.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20260901' AND '20260907'
QUALIFY ROW_NUMBER() OVER (
  PARTITION BY user_pseudo_id
  ORDER BY event_timestamp DESC
) = 1;

ROW_NUMBER() ranks each user's events from newest to oldest.

QUALIFY then keeps only #1, the user's most recent event.

Why this is useful with GA4 data

You can use the same pattern to find:

✓ A user's latest event
✓ Their most recent traffic source
✓ Their last purchase
✓ The latest event in each session
✓ The most recent value of a user property

The BigQuery trick: WHERE filters rows before window functions are calculated. QUALIFY lets you filter based on the result of a window function.

Cleaner SQL. Fewer unnecessary subqueries.

Thank you,
Anil Batra, Founder Optizent

P.S. Want to become more confident with BigQuery? Enroll in my BigQuery for Marketers and Marketing Analysts course.

This Week's BigQuery Insight: UNNEST Creates More Rows

Anil Batra — Thu, 23 Jul 2026 16:21:08 +0000

Have you ever wondered why some BigQuery queries against GA4 data are unexpectedly slow?

The answer is often a single word: UNNEST.

GA4 stores parameters in a nested structure. Every time you use UNNEST(event_params), BigQuery creates additional rows behind the scenes.

Imagine a single event has these parameters:

page_location
page_title
page_referrer
session_id
source

That one event is now treated as five rows after UNNEST.

If your table contains 10 million events with an average of 15 parameters each, you've just turned 10 million rows into 150 million rows.

That's why query performance can suddenly degrade.

Instead of unnesting everything:

SELECT *
FROM `project.analytics_123456.events_*`,
UNNEST(event_params)

Ask yourself: "Do I really need every parameter, or just one?"

If you only need page_location, consider extracting just that value:

SELECT
  (
    SELECT value.string_value
    FROM UNNEST(event_params)
    WHERE key = 'page_location'
  ) AS page_location
FROM `project.analytics_123456.events_*`

The lesson this week: Every UNNEST has a cost.

Understanding how nested data expands in BigQuery will make you a faster, more efficient analyst, especially when working with GA4 exports.

Remember: The biggest BigQuery optimizations often come from processing fewer rows, not writing more complex SQL.

Thank you,

Anil Batra, Optizent Academy

P.S. Want to become more confident with BigQuery? Enroll in my BigQuery for Marketers and Marketing Analysts course.

P.P.S. Using GA4? Make sure you're analyzing good data. Run a quick audit with GA Auditor to identify common implementation and tracking issues.

This Week's BigQuery Insight: Use LIMIT While Exploring Data

Anil Batra — Fri, 17 Jul 2026 17:19:32 +0000

When you're writing a new query, don't try to analyze millions of rows immediately.

Start by adding a LIMIT clause so you can quickly inspect the results and verify that your logic is correct.

SELECT
  event_date,
  event_name,
  user_pseudo_id
FROM `my_project.analytics_123456.events_*`
WHERE event_name = 'page_view'
LIMIT 100;

This won't reduce the amount of data scanned, but it will help you:

Validate your query faster
Catch mistakes early
Iterate more efficiently
Build confidence before running more complex analyses

Many BigQuery users spend more time fixing incorrect queries than writing them. Developing the habit of testing with a small set of results can save a lot of frustration.

Takeaway: Before building a dashboard or report, make sure your query returns the expected data.

See you next week with another BigQuery insight.

Thank you,
Anil Batra

Next Action:

1. If you're learning BigQuery for analytics, my BigQuery course walks through real-world examples using GA4 export data.
2. If you are using GA4, then make sure GA4 is set up properly. You can run a free scan on GAAuditor.com

This Week's BigQuery Insight: Learn to Read Queries Backwards

Anil Batra — Thu, 02 Jul 2026 16:38:18 +0000

When you're learning SQL, don't read queries from top to bottom.

Instead, read it in this order:

FROM → WHERE → GROUP BY → SELECT → ORDER BY

FROM – Where is the data coming from?
WHERE – Which rows are being filtered?
GROUP BY – How is the data being grouped?
SELECT – What columns or calculations are being returned?
ORDER BY – How are the results being sorted?

This approach mirrors how SQL is logically processed, making even complex queries much easier to understand.

For example:

SELECT country,
       COUNT(*) AS users
FROM `my_dataset.users`
WHERE signup_date >= '2026-01-01'
GROUP BY country
ORDER BY users DESC;

Read it like this:

FROM → Start with the users table.
WHERE → Keep only users who signed up this year.
GROUP BY → Group them by country.
SELECT → Count the users in each country.
ORDER BY → Show countries with the most users first.

Try it with every query you read this week, and you'll be surprised how quickly your SQL skills improve.

If you'd like to learn BigQuery and SQL through practical, real-world examples (especially using GA4 data), check out my BigQuery course.

Enjoyed this tip? Invite your friends to this newsletter.

If you are using GA4, check out GA Auditor, a platform that ensures you are collecting the right data.

Your BigQuery Analysis Is Only as Good as Your GA4 Data

Anil Batra — Tue, 16 Jun 2026 21:49:44 +0000

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
✓ Ecommerce tracking
✓ User properties
✓ Traffic attribution
✓ Cross-domain measurement
✓ Consent Mode
✓ Google Tag Manager implementation
✓ Data quality issues
✓ Reporting accuracy
✓ And much more

Whether you're an analyst, engineer, consultant, or marketer, this checklist can help you catch issues before they become expensive reporting problems.

👉 Download the Free 150+ GA4 Audit Checklist

Thanks,
Anil Batra

Something I kept noticing while working with BigQuery

Anil Batra — Thu, 16 Apr 2026 22:30:01 +0000

Over the past few years, I’ve spent a lot of time helping teams use BigQuery to answer more advanced business questions.

But it always comes back to one thing:

Before you start writing queries or building reports, you need to make sure the right data is actually being collected.

Because if the underlying tracking isn’t set up correctly, even the most advanced analysis in BigQuery won’t lead to the right insights.

That’s what I’ve been focusing on lately.

I have built a simple tool to help with that. It checks your GA4 and GTM setup and highlights potential gaps that could impact your data quality.

👉 You can try it here: GAAuditor.com

It makes your BigQuery work more valuable—because you can trust the data you’re analyzing.

Longer term, I’m exploring ways to make it easier to go from reliable data → to answering real business questions without so much manual effort of writing SQL queries. I’ll share more on that soon.

Also, if you’re not already part of it, I will now be sharing all my updates, ideas, and tools here: 👉 https://insights.optizent.com/subscribe

More soon.

– Anil Batra
Optizent.com

P.S. Click here to sign up for GAuditor.com

Unlock GA4 Insights: Live BigQuery Training on Event Parameters

Anil Batra — Tue, 16 Sep 2025 18:57:59 +0000

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
💻 Format: Live Online Class

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.

👉 Reserve Your Spot Now

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

See you live!
Anil Batra, Founder Optizent Academy

SQL For Beginners Free Live Session

Anil Batra — Mon, 02 Jun 2025 19:54:33 +0000

I am conducting a free live session for Beginners to Learn the basics of SQL.

If you haven’t yet started with SQL or are struggling to make sense of SQL and GA4 data in BigQuery, then attend this live session.

Here is the link to save your spot and get the Zoom link: https://academy.optizent.com/live-webinar/sql-fundamentals-for-beginners/register.

Thank you,
Anil Batra, Optizent Academy
P.S. If you can’t attend the live session, then join the Digital Analytics Live Session Vault to get access to all of my live training sessions.

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Traffic Source Fields in GA4 BigQuery Schema Demystified

Anil Batra — Thu, 10 Apr 2025 23:45:15 +0000

When you export GA4 data to BigQuery, you will find several source, medium, and other data fields related to traffic. In this post, I will help you understand what type of information in these fields so you can choose the right one for your data analysis.

There are three columns of the record type that contain traffic-related information. These fields are

traffic_source
collected_traffic_source
session_traffic_source_last_click

Let's look at each of these fields.

traffic_source

This record field contains the details about the first traffic source that acquired the user. These values do not change if the user interacts with subsequent campaigns.

You can use this information to get the same information that you get in the User Acquisition report in GA4.

collected_traffic_source

This field contains the traffic source data that is available with each event. For example, if a session first start the journey from Google search engine, then session_start and other events will contain this information. However, if, in the middle of the session, the user decides to go back to Bing and then find the link to come back to the session, any events following that will contain information related to Bing.

This field can help you look at the traffic information at the event level so that you can analyze if users go back to another traffic source during their visits.

session_traffic_source_last_click

The session_traffic_source_last_click record contains the last-click attributed session traffic source data across Google ads and manual contexts, where available.

This is the information that will match your Session acquisition report in GA4.

If you want to see the entire list of data fields within these records, then see the post at https://www.optizent.com/blog/traffic-source-fields-in-ga4-bigquery-schema-demystified/

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GA4 + BigQuery: 2 Videos to Help You Get Started with Confidence

Anil Batra — Sat, 14 Dec 2024 19:10:15 +0000

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:

Best regards,
Anil Batra
Optizent Academy

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Anil Batra — Sun, 01 Dec 2024 19:03:47 +0000

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Optizent Academy

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Anil Batra — Thu, 28 Nov 2024 08:17:23 +0000

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Create Looker Studio Reports with GA4 Data in BigQuery

Anil Batra — Sat, 19 Oct 2024 19:25:15 +0000

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

Go to Looker Studio and click on "Blank Report" to create a new report.
You will be promoted to "Add data to report".
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.
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.
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.
Next click on the "Add" button in the top right bottom.
You might be promoted with a message confirming that you are adding a data to the report. Click on "ADD TO REPORT" button.
Now the data sources is added. You will see that Looker Studio automatically creates one table chart for you.
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.
Not the template that we picked in the step above will help you get the names of the paramater, counts of users etc.
To add additional tables from BigQuery (or any other data source), click on "Resource" menu option on top.
Select "Manage added data sources". This will show you all the data sources that you have added in your report.
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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How to Use Google AI Model, Gemini in BigQuery

Anil Batra — Fri, 11 Oct 2024 18:57:13 +0000

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.

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Enable Google AI Model, Gemini in BigQuery

Anil Batra — Thu, 26 Sep 2024 17:38:34 +0000

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.

How to Enable Gemini in BigQuery

Enable necessary APIs.
Grant required Identity and Access Management (IAM) roles.
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.

Go to Gemini for Google Cloud page.
Click Enable.
You will see the status as Enabled and now Gemini is available in BigQuery in the selected Google Cloud project.

IAM Permissions

Go to the IAM & Admin page.
Click on View by principals.
In the Principal column, find the user (principal) for which you want to enable access to Gemini in BigQuery
Click edit Edit principal in that row.
In the Edit access pane, click add Add another role.
In the Select a role list, select Gemini for Google Cloud User.
This will grant the Cloud AI Companion User IAM role (roles/cloudaicompanion.user) to the user.

Turn on BigQuery Features in Your Project

In the Google Cloud console, on the project selector page, select the Google Cloud project.
In the Google Cloud console, go to the BigQuery page.
In the toolbar, click the pen with sparks (Gemini) drop-down icon.
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.

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Anil Batra — Thu, 19 Sep 2024 18:11:29 +0000

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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 skills

Anil Batra — Tue, 30 Jul 2024 06:55:33 +0000

Upgrade your BigQuery and SQL Skills

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Advanced Content
Readymade SQL Scripts

BigQuery Table Explorer

Anil Batra — Fri, 26 Jul 2024 15:14:24 +0000

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

Exceptions: Nested record type columns such as event_params are not supported in this feature.
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.
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.
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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Purchases not showing up in GA4

Anil Batra — Wed, 24 Jul 2024 20:22:49 +0000

Recently we were auditing a GA4 account but no purchase data was showing up.

The natural way for anybody to debug that is to make sure

Purchase event is firing
Make sure it is sending the right parameters
Make sure it is sending to the right GA4 property
and so on.

And that’s exactly were the first steps. This setup was a bit more complicated because purchase data was sent via server-side tracking. So we did check the server-side setup as well and it looked fine.

Everything looked fine. However no data was showing up in GA4.

Our default audit setup also includes sending the GA4 data to BigQuery. In this case the client had that already setup so we could see the past data.

And guess what? The purchase data was in BigQuery but not in GA4.

So GA4 was hiding that data, which happens in case of thresholding but there there was no thresholding message shown in GA4.

So BigQuery confirmed that indeed all the purchase data was being sent to GA4 and there was no issue with purchase tags, trigger or values. Having this knowledge shifted our focus from debugging tag firing and parameters to why GA4 is withholding that information.

Further digging into BigQuery data showed us the reason.

There was an issue with user id setup.

Now I can’t go into every single detail because every setup in different and every issue is different but the point of this post is to tell you the important of looking at the raw data because aggregated data in GA4 does not tell you the full story.

Ability to send GA4 data to BigQuery is one of the best features of GA4 . Don’t let it go waste. Having the data in BigQuery gives deeper insights and debugging capabilities that are just not possible with GA4 interface.

So if you haven’t yet started with BigQuery then do it right away. You will be amazed how much time and effort you save while debugging GA4.

(Note: I do cover such cases and go over the details of how we solved them in Optizent Inner Circle Membership).

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GA4 To BigQuery in 3 Simple Steps

Anil Batra — Fri, 19 Jul 2024 16:17:37 +0000

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

Create a BigQuery Account and Project in Google Cloud
Enable GA4 to BigQuery Setting in GA4
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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BigQuery Table Explorer

Video of Table Explorer

A few more things to know

Use AI as Your Personal Assistant

Purchases not showing up in GA4

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GA4 To BigQuery in 3 Simple Steps

GA4 to BigQuery in 3 Simple Steps

Step 1: Create a BigQuery and Project In Google Cloud

Step 2: Enable GA4 + BigQuery integration

Step 3: Enable BigQuery APIs.

Watch The GA4 to BigQuery in 3 Steps Video

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