Solving the GA4 Looker Studio Quota Error: Export GA4 Data to BigQuery

If you've been using Google Analytics 4 (GA4) with Looker Studio (formerly Google Data Studio) for reporting and analysis, you may have encountered the frustrating "too many requests" quota error. This error occurs when the number of API requests made by Looker Studio to the GA4 API exceeds the allocated quota, hindering your ability to create and update reports effectively.

Finding a solution to this quota error is crucial for marketers and analysts who rely on timely and accurate data to make informed decisions. Without a reliable reporting system, it becomes challenging to monitor performance, optimize campaigns, and communicate results to stakeholders.

Fortunately, there is a solution to this problem: exporting your GA4 data to BigQuery. By storing your data in BigQuery, you can bypass the API quota limitations and recreate your reports using the powerful querying capabilities of BigQuery. In this blog post, we'll guide you through the process of exporting your GA4 data to BigQuery and provide examples of how to recreate common reports using SQL queries.

Understanding the GA4 Looker Studio Quota Error

The "too many requests" error in GA4 Looker Studio occurs when the number of API requests sent from Looker Studio to the GA4 API exceeds the allocated quota. GA4 has specific API quotas in place to manage the usage and prevent overload on their servers. These quotas are divided into different categories, such as Core, Realtime, and Funnel, each with its own set of limits.

Looker Studio creates an API request for each chart and filter in your reports. For example, if you have a report with five charts and two filters, Looker Studio will make multiple API requests to fetch the required data. If you have several users accessing the report simultaneously or have set up frequent auto-refreshes, the number of API requests can quickly add up and exceed the allocated quota.

The GA4 API quotas are based on factors such as the number of tokens consumed per property per day, per hour, and per project. Each request consumes a certain number of tokens depending on its complexity, determined by factors like the number of rows, columns, and the date range. Once the token quota is exhausted, the "too many requests" error is triggered, preventing further API requests from being processed.

This quota error can significantly impact your reporting and analysis workflow. When the error occurs, your Looker Studio reports may fail to load or update, leaving you without access to the latest data. This can lead to delays in decision-making, as well as difficulty in sharing up-to-date insights with your team or clients. Additionally, troubleshooting the quota error can be time-consuming, taking away valuable resources from other essential tasks.

To overcome this challenge and ensure uninterrupted reporting and analysis, exporting your GA4 data to BigQuery provides a reliable solution. By querying your data directly in BigQuery, you can bypass the API quota limitations and create reports that are not dependent on the GA4 API's availability or quota restrictions.

Exporting GA4 Data to BigQuery

BigQuery is a powerful, cloud-based data warehousing solution provided by Google Cloud Platform. It allows you to store and query massive datasets, making it an ideal solution for storing and analyzing your GA4 data. By exporting your GA4 data to BigQuery, you can overcome the limitations imposed by the GA4 API quotas and ensure a more reliable and scalable reporting process.

Benefits of using BigQuery for GA4 data include:

1. Scalability: BigQuery can handle petabytes of data, ensuring that your data storage and querying needs are met as your business grows.
2. Speed: With its powerful querying capabilities, BigQuery can process and return results for complex queries in seconds, enabling faster data analysis and reporting.
3. Flexibility: BigQuery supports SQL-like queries, allowing you to customize your data analysis and create reports tailored to your specific needs.
4. Integration: BigQuery seamlessly integrates with other Google Cloud Platform services, such as Google Data Studio, making it easier to visualize and share your data insights.

To export your GA4 data to BigQuery, follow these step-by-step instructions:

Setup a GCP project for BigQuery

Before you can link your GA4 property to BigQuery, you need to create a Google Cloud Platform (GCP) project. Follow these steps to create a new GCP project:

1. Go to the Google Cloud Console (https://console.cloud.google.com/).
2. If you haven't used GCP before, you may need to set up a new account and provide billing information. If you already have a GCP account, proceed to the next step.
3. In the top navigation bar, click on the project dropdown menu and select "New Project."
4. In the "New Project" window, enter a unique name for your project. This name will be used to identify your project across all GCP services.
5. (Optional) If you want to organize your projects under a specific folder or organization, you can select the appropriate option in the "Location" dropdown menu. If you don't have any folders or organizations set up, leave the default selection as is.
6. Click on the "Create" button to create your new GCP project.
7. After a few moments, your new project will be created, and you'll be redirected to the project dashboard.

Linking GA4 Property to BigQuery

Now that you have created a GCP project, you can proceed with the steps to link your GA4 property to BigQuery:

1. In the Google Analytics UI, navigate to the "Admin" section.
2. Click on the "BigQuery Linking" option under the "Property" column.
3. Click on the "Link" button to connect your GA4 property to BigQuery.
4. Select the Google Cloud Project you want to use or create a new one.
5. Grant the necessary permissions to allow GA4 to write data to BigQuery.

By following these steps, you'll have successfully linked your GA4 property to BigQuery, created a dataset to store your exported data, and set up a data export stream. Once your data starts flowing into BigQuery, you can begin recreating your reports using SQL queries, which we'll cover in the next section.

Recreating Common GA4 Reports in Looker Studio with BigQuery

Now that your GA4 data is being exported to BigQuery, you can start recreating your reports using SQL queries. If you're not familiar with SQL, don't worry! We've prepared a YouTube video tutorial that will guide you through the process of recreating common GA4 reports step by step.

In this section, we'll provide examples of SQL queries for recreating four common GA4 reports: Audience Overview, Acquisition, Behavior, and Conversions. You can use these queries as a starting point and customize them according to your specific reporting needs.

Audience Overview Report

SELECT COUNT(DISTINCT user_pseudo_id) AS users, COUNT(DISTINCT (SELECT value.int_value FROM UNNEST(event_params) WHERE KEY='ga_session_id')) AS sessions, COUNT(DISTINCT,CASE WHEN ( SELECT value.int_value FROM UNNEST(event_params) WHERE KEY='ga_session_number') = 1 THEN user_pseudo_idEND) AS new_users,SAFE_DIVIDE(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),COUNT(DISTINCT CONCAT(user_pseudo_id,(SELECT value.int_value FROM UNNEST(event_params) WHERE KEY = 'ga_session_id')))) AS engagement_rate FROM `rosy-hangout-359301.analytics_394939764.events_*`

The Audience Overview report provides a high-level summary of your website's audience, including metrics like users, sessions, and bounce rate. To recreate this report in Looker Studio using BigQuery, you'll need to write an SQL query that selects the necessary metrics from the events_* table in your BigQuery dataset, calculates the bounce rate, and filters the data for a specific date range.

Acquisition Report

First Touch Source

SELECT traffic_source.source AS first_touch_source, traffic_source.medium AS first_touch_medium, traffic_source.name AS first_touch_campaign, COUNT(DISTINCT (select value.int_value FROM UNNEST(event_params) where key='ga_session_id')) AS sessions FROM `rosy-hangout-359301.analytics_394939764.events_*` GROUP BY 1, 2, 3 ORDER BY 4 DESC

Session Source

SELECT  ifnull((SELECT value.string_value FROM unnest(event_params) where key = 'source'), '(direct)') session_source, ifnull((SELECT value.string_value FROM unnest(event_params) where key = 'medium'), '(none)') AS session_medium, ifnull((SELECT value.string_value FROM unnest(event_params) where key = 'campaign'), '(direct)') AS session_campaign,  COUNT(DISTINCT (select value.int_value FROM UNNEST(event_params) where key='ga_session_id')) AS sessions FROM `rosy-hangout-359301.analytics_394939764.events_*` GROUP BY  1, 2, 3 ORDER BY 4 desc

The Acquisition report shows how users arrive at your website, including the source, medium, and campaign. To recreate this report, your SQL query will need to select the source, medium, and campaign dimensions from the events_* table, along with the number of sessions. The data should be grouped by the dimensions and filtered for a specific date range.

Behavior Report

SELECT SPLIT((SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location'), '?')[SAFE_OFFSET(0)] AS page_location, SUM(case when event_name = 'page_view' then 1 else 0 end) AS pageviews, COUNT(DISTINCT case when event_name = 'page_view' then user_pseudo_id end) AS unique_pageviews, AVG((SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'engagement_time_msec')) / 1000 AS avg_time_on_page FROM `rosy-hangout-359301.analytics_394939764.events_*` GROUP BY page_location

The Behavior report provides insights into how users interact with your website, including pageviews, unique pageviews, and average time on page. To recreate this report, your SQL query will need to select the page location from the event_params array, calculate pageviews, unique pageviews, and average time on page. The data should be filtered for the 'page_view' event and a specific date range, and grouped by page location.

Conversions Report

SELECT event_date,  SUM(CASE WHEN event_name in ('form_submit','lead') then 1 end) AS conversions,  SUM(case when event_name = 'session_start' then 1 end) as sessions, FROM `rosy-hangout-359301.analytics_394939764.events_*` GROUP BY 1

The Conversions report shows the number of conversions and conversion rate for specific goals. To recreate this report, your SQL query will need to select the page location for the 'purchase' event, calculate the number of conversions and conversion rate. The conversion rate should be calculated by dividing the number of conversions by the total number of pageviews. The data should be filtered for a specific date range and grouped by page location.

By recreating these common GA4 reports in Looker Studio using BigQuery, you can ensure that your reporting remains uninterrupted even when faced with GA4 API quota limitations. Remember to customize your SQL queries to include additional dimensions, metrics, or filters based on your specific reporting requirements.

Stay tuned for our upcoming YouTube video tutorial, where we'll walk you through the process of writing these SQL queries and recreating your GA4 reports in Looker Studio step by step.

Connecting BigQuery to Looker Studio

Once you have your GA4 data in BigQuery and have written the necessary SQL queries to recreate your reports, the next step is to connect BigQuery to Looker Studio as a data source. This will allow you to visualize your data and create informative, visually appealing reports.

Here's a step-by-step guide on connecting BigQuery to Looker Studio:

1. Open Looker Studio (https://lookerstudio.google.com/) and click on the "+" button to create a new report.
2. In the "Connect to data" section, click on the "BigQuery" option.
3. Select the Google Account associated with your BigQuery project.
4. Choose the project, dataset, and table that contain your GA4 data.
5. Click on the "Connect" button to establish the connection between BigQuery and Looker Studio.

Once connected, you can start building your reports using the data from BigQuery. To create visually appealing and informative reports, consider the following tips:

1. Use a clear and consistent layout that makes it easy for users to navigate and understand the data.
2. Choose appropriate chart types that effectively communicate the insights you want to convey.
3. Use colors and fonts that align with your brand guidelines and create a professional look.
4. Add filters and date range selectors to allow users to interact with the data and explore different segments.
5. Provide clear titles, labels, and annotations to guide users through the report and highlight key insights.

By following these tips and leveraging the power of BigQuery and Looker Studio, you can create impactful reports that drive data-informed decision-making for your organization.

Conclusion:In this blog post, we discussed the GA4 Looker Studio quota error and how it can hinder your ability to create and update reports effectively. We introduced BigQuery as a solution for storing and querying your GA4 data, bypassing the API quota limitations.

We provided a step-by-step guide on exporting your GA4 data to BigQuery, including linking your GA4 property, creating a BigQuery dataset, and setting up data export. We also covered the process of recreating common GA4 reports, such as Audience Overview, Acquisition, Behavior, and Conversions, using SQL queries in BigQuery.

We encourage you to try exporting your GA4 data to BigQuery and recreating your reports using the provided SQL queries. By doing so, you can ensure uninterrupted reporting and analysis, even when faced with GA4 API quota limitations.

To further assist you in this process, we've created a YouTube video tutorial that walks you through the steps of writing SQL queries and recreating your GA4 reports in Looker Studio. Be sure to watch the tutorial to gain a more in-depth understanding of the process and explore advanced reporting techniques.

If you have any questions or would like to share your experiences with exporting GA4 data to BigQuery and recreating reports in Looker Studio, please leave a comment below. We value your feedback and are always happy to help!