Exploring the limitations of Google Analytics 4 (GA4) API

Google Analytics 4 (GA4) represents a significant evolution in the world of web and app analytics, offering advanced features and capabilities compared to Google Universal Analytics.

As of July 1st, 2023, Universal Analytics ceased processing new hits, marking a pivotal transition to Google Analytics 4 (GA4) for users. This change is not limited to website analytics; it extends to broader aspects, including implications for integrating with tools like Google BigQuery and Looker Studio (previously known as Google Data Studio). Users have encountered a lot of challenges in loading data from GA4 into Looker Studio in early 2013.

Understanding the reasons behind these challenges and exploring solutions is essential for a smooth transition and continued effective use of analytics tools.

Note: This post was originally published in Feb 2016 to specify the advantages, benefits, and challenges of Google Analytics API and was completely updated in January 2024 for accuracy and comprehensiveness regarding Google Analytics 4 API.

The GA4 API, also known as the Google Analytics Data API, is a native tool that grants programmatic access to reports data from Google Analytics 4. This access opens you up remarkable opportunities for in-depth analysis of user interactions across various platforms, including websites, iOS, and Android apps in third-party tools (eg. data visualization with Looker Studio by Google) based on GA4 data.

Consider the types of insights you can get with the GA4 API. For instance, you could generate a report detailing the number of views each of the top 10 URLs on your website received over the past 365 days. Or, you might analyze the daily active users on your IOs application over the previous week.

This API isn't just limited to getting custom reports data. Its versatility extends to using the data to craft personalized dashboards, automating report generation, and integrating analytics data with other business tools for a more comprehensive view.

On the technical front, leveraging the GA4 API involves executing calls to a range of methods. Key methods include runReport for standard reporting, runPivotReport for multidimensional analysis, getMetadata for accessing information about available metrics and dimensions, runRealtimeReport for accessing real-time data, and runFunnelReport for funnel analysis. Each method plays a critical role in different aspects of data reporting and analysis.

Google Analytics API and GA4 API represent two different stages in the evolution of Google's analytics platforms. The original Google Analytics API, linked with Universal Analytics, provides traditional website tracking and reporting export features, focusing on session-based data and offers extensive compatibility with various web technologies.

On the other hand, GA4 API, associated with Google Analytics 4, adopts an event-based data model, which is more adaptable to both web and app environments. GA4 emphasizes user engagement and cross-platform tracking, offering more advanced machine learning insights. This shift reflects a broader industry trend towards comprehensive, user-centric analytics.

Universal Analytics offers several APIs, each with distinct functionalities:

• Core Reporting API (v3): This API allows users to access most of the data from Google Analytics reports. It's useful for creating custom reports and automations. This version (v3) is associated with Universal Analytics and offers robust, session-based data retrieval capabilities.

• Unsampled Data API: This API provides access to unsampled data in Google Analytics. Normally, for large data sets, Google Analytics uses sampling to estimate data trends, which can sometimes compromise accuracy. The Unsampled Data API allows for the extraction of full data sets, ensuring more precise and reliable reporting, particularly beneficial for high-traffic websites.

• Real-Time Reporting API: This API allows developers to access real-time data about what's happening on their website or app. It can display active users, their geographic locations, the pages or events they are interacting with, and more. This is particularly useful for monitoring immediate effects of marketing campaigns, website changes, or events.

After the recent transition to GA4 Among the APIs mentioned - Core Reporting API (v3), Unsampled Data API, and Real Time Reporting API - none are directly compatible with Google Analytics 4 (GA4). These APIs are designed for use with Universal Analytics, the predecessor to GA4.

For GA4, Google has introduced new APIs, such as the Google Analytics Data API (v1), which is designed to work with the event-based data model and advanced features of GA4. This API provides access to both standard and custom reports, reflecting the more flexible and user-centric approach of GA4.

If you're transitioning to or already using GA4, you'll need to use the APIs specifically designed for it, rather than the ones used for Universal Analytics.

GA4 API quota limitations are an essential aspect of Google Analytics 4, designed to ensure fair and efficient use of resources among all free users. These quotas are categorized into three types: Core, Realtime, and Funnel.

Each type of request — whether it's to Core or Real-time methods — draws from its respective quota. This separation means that one request won't consume quotas from both categories.

Two key quotas impacting Looker Studio reports are concurrent requests and hourly tokens:

• Concurrent Requests: This quota is determined by the number of simultaneous viewers accessing reports. As more users view reports at the same time, the quota limit is reached more quickly.

• Hourly Tokens: The complexity and frequency of interactions with the report affect this quota. Factors like the number of visualizations, the complexity of the data being consumed, and the use of filters contribute to the consumption of hourly tokens.

Understanding these quotas is crucial for users who rely on GA4 for detailed analytics and reporting, as exceeding these limits can restrict data access and require modifications in data requests strategies. For more detailed information, the official Google Analytics documentation offers an extensive list and explanation of these quotas.

Google Analytics 4 implements API quotas as a means to maintain equitable access for all users while safeguarding the system's performance.

The GA4 team highlights two key reasons for these quotas:

• Resource Management:

Google's infrastructure, despite being robust, operates with a finite number of servers. Unlimited user access could potentially deplete Google's computing power, adversely affecting service performance. API quotas are therefore essential in ensuring that all developers have fair access to resources without overburdening the system.

• Protection Against Abuse:

The quotas in GA4 also serve as a protective measure against system abuse. Instances like a faulty program incessantly consuming tokens can monopolize Google’s resources, impeding service availability for other users engaged in meaningful activities. By enforcing quotas, Google can prevent such scenarios, ensuring its services remain accessible and reliable for all users.

As with many digital marketers or website operators, you're likely familiar with the frequent emails from Google Analytics. These aren't just the usual monthly reports but also reminders about the transition from Universal Analytics to Google Analytics 4 (GA4).

Introduced in October 2020, GA4 has been available for some time, allowing users the option to switch from the older Universal Analytics version. During this period, Looker Studio (formerly Google Data Studio) has been used to extract data and compile marketing reports.

Utilizing the Google Analytics API rather than the standard interface expanded the possibilities for gaining insights, enabling a deeper dive into hierarchical traffic analysis and time-specific attribution.

However, challenges emerged with the release of Google Analytics 4 API v1 in March 2022. While there has always been a quota for API usage per service account, Google began to enforce these limits more stringently from November 2022. This tightening of restrictions is likely the reason behind any API quota errors you're encountering within your Looker Studio dashboards.

The top five errors encountered while using Google Analytics 4 (GA4) API, particularly in conjunction with Looker Studio, primarily relate to quota limits and data fetching issues:

• Data Quota Limit Exceeded: This occurs when the data quota for a GA4 property is surpassed. GA4 has different quotas for its Standard and 360 versions, and exceeding these limits results in an inability to visualize data in Looker Studio until the quota resets.

• Failure in Data Retrieval: When the property-level quota of GA4 is reached, all users associated with that property are affected. This means if the quota is consumed by either Looker Studio reports or other data sources, Looker Studio may be unable to access new data.

• API Request Quota Exceeded: This error indicates that the allowed number of API requests has been reached. GA4's API permits data retrieval in two date ranges per request, and reaching this limit impedes further data access.

• Concurrent Request Quota Exhausted: This error arises when the number of simultaneous API requests exceeds the limit, which is typically 10 for non-paid GA properties. Exceeding this limit in Looker Studio, either through complex reports or multiple users making concurrent requests, triggers this error.

• Generic Error Message: Sometimes, a generic error message appears due to authentication issues. While switching to an older authentication method like MD5 might resolve this, it's not generally recommended due to increased security vulnerabilities compared to more modern methods like scram-sha-256.

In data analysis and reporting, users often encounter the challenge of adhering to the API quota limits set by Google Analytics 4 (GA4). These restrictions can pose hurdles in accessing and leveraging the full potential of GA4 data.

To circumvent these limitations, several strategies and tools are being employed, offering innovative and efficient ways to import and manage data without breaching the set GA4 API quotas. This approach ensures that the rich insights offered by GA4 are fully utilized, enhancing data-driven decision-making processes.

OWOX BI provides a streamlined approach for accessing and managing website analytics and tracking data, effectively overcoming the limitations of native Google connectors. It features a direct data collection from your website to Google BigQuery, which simplifies the process of getting real-time reports and dashboards.

No coding or developers are required for setup, making it a user-friendly option for various users. By simply logging in and linking their website data to adspend data, preparing for reporting and visualizing on the dashboards (templates are already prepared), you can start generating insightful analytics.

The platform excels in data replication and preparation, offering swift automation and reporting capabilities. It enables users to clean, organize, and prepare the report data before further visualization.

• Fixing Tracking Issues with Direct/None Traffic: OWOX BI provides a server-side tracking system out of the box, that extends the lifespan of cookies, allowing marketers to see the whole conversion journey accurately.
• Privacy-Centric Solution: OWOX BI Streaming is a privacy-centric approach to collect and use personally identifiable information (PII). With OWOX BI, you can choose data residency (EU, US, or global), remove PII for non-consent users, and encrypt all user data with custom keys.
• Address Missed Non-Consent Data: OWOX BI also offers a unique solution to address the non-consent gap in the reports, providing the real-time common behavioral data for non-consent users (without PII).
• Data Completeness: Report value is based on the quality of gathered data. That is why it is important to ensure that your data collection works without data distortion, so you have all the information to make the decisions based on the understanding of "which items are driving sales?", "which content is driving sales?", etc. OWOX BI has no limits for the amount of data collected, or the number of user-scoped custom dimensions, event parameters, or URL length.
• Real-time data collection without unexpected delays: OWOX BI offers the real-time on-site data collection right into your data warehouse (DWH) without any delays or “gaps” in data. This allows you to get the operational data for prompt decision-making, driven by up-to-the-minute data, paves the way for optimizing business processes and increasing profitability in real-time.
• True Acquisition Sources: With OWOX BI, you can be sure that you know what truly drives your conversions and overcome cookie lifespan restrictions and GA4 limitations
• 100% data ownership: Never worry about your data ending up in the wrong hands again. OWOX BI guarantees 100% data ownership for every client.
• No data sampling: OWOX BI doesn’t use data sampling on any of our plans. Collect unlimited website user behavior data
• Customizable Report Templates: OWOX BI offers customizable report templates, which users can create and utilize according to their specific needs. These templates allow for the connection of multiple reports as necessary, ensuring real-time data accessibility and flexibility in report generation.
• Cross-Channel Reporting: For analytics needs that extend beyond Google Analytics, OWOX BI provides seamless integration with multiple data sources. This feature is invaluable for cross-channel campaign reporting, as it allows for the combination of metrics from various sources like Facebook Ads, Twitter Ads, Linkedin Ads, Bing Ads and Google Ads into a single, comprehensive report. You can merge those data with the conversions from the website or with the sales from the CRM. So you get a more holistic view of your digital marketing efforts.

And so much more…

In data analytics, the integration of Google BigQuery with Google Analytics 4 (GA4) marks a significant advancement, offering a powerful solution to overcome the API limits inherent in GA4. This integration brings the BigQuery Export feature to all GA4 properties, democratizing a capability that was once exclusive to Google Analytics 360 license holders. BigQuery, Google's cloud-based big data analytics service, is renowned for its rapid data processing and sophisticated analysis capabilities.

• Automated Data Export: With GA4, data is automatically exported to BigQuery. This seamless integration simplifies the data management process, ensuring that users have up-to-date data for analysis without manual intervention.
• Advanced Code-Based Data Manipulation: Users can utilize SQL-like queries and SDKs for in-depth data manipulation. This feature caters to those who prefer working with SQL, providing a familiar and powerful environment for data analysis.
• Access to Raw Event-Level Data: The integration provides comprehensive access to raw, event-level GA4 data, along with various collected parameters. This level of detail is crucial for in-depth analytics and understanding user behaviors.
• Custom Reporting and Analysis: Users can create custom reports and perform intricate analyses using SQL queries. This flexibility allows for tailored insights that align with specific business needs and objectives.
• Handling Large Datasets: Leveraging BigQuery's advanced data processing capabilities enables users to efficiently handle large datasets. This is particularly beneficial for businesses dealing with vast amounts of data, ensuring smooth and quick data analysis.
• Oh, by the way, OWOX BI has the templates to sessionize raw [GA4] BigQuery export data, so you can then attribute adcosts to sessions and easily merge conversions with costs.

Despite its numerous advantages, there are certain limitations to the Google BigQuery Export:

• Complexity for Non-Technical Users: The use of SQL-like queries and SDKs might be challenging for non-technical users. There's a steep learning curve for those unfamiliar with these tools.
• Cost Implications: While BigQuery offers a powerful platform for data analysis, it can incur significant costs, especially when processing large datasets or performing complex queries.
• Data Privacy and Security: Handling sensitive user data in BigQuery necessitates strict adherence to data privacy and security protocols. Organizations must be vigilant in managing access and ensuring compliance with data protection regulations.

For businesses utilizing Google Analytics 4 (GA4) that frequently encounter quota limitations, upgrading to GA4 Premium emerges as a potent solution. This upgrade is tailored to significantly augment quota limits, offering an increase that is approximately tenfold. This enhanced capacity is especially beneficial for clients dealing with high volumes of data and those in need of more extensive data processing capabilities.

• Increased Quota Limits: The primary advantage of GA4 Premium is the substantial increase in quota limits. This expansion allows for more extensive data collection and analysis, crucial for businesses with large-scale analytics needs.

• Additional Features and Benefits: GA4 Premium not only increases quota limits but also offers additional features that can enhance data analytics capabilities. These features are designed to provide more in-depth insights and a better overall understanding of data trends.

However, there are significant considerations to keep in mind before deciding to upgrade:

• Cost Implications: GA4 Premium comes with a notable price tag, of approximately $30,000 per year. This cost factor is a critical consideration, especially for small to medium-sized businesses or those with limited budgets.

• Cost-Benefit Analysis: The decision to upgrade should be based on a thorough cost-benefit analysis. Businesses need to assess whether the additional quota and features provided by GA4 Premium justify the investment, considering their specific analytics needs and financial constraints.

• Not a One-Size-Fits-All Solution: While GA4 Premium offers an immediate solution to quota limitations, it might not be the ideal option for all users. Businesses should evaluate other potential solutions, such as optimizing their current usage of GA4 or exploring alternative analytics tools that might offer a more cost-effective solution.

In data analytics with Google Analytics 4 (GA4), navigating around the data limits poses a significant challenge for many businesses. A practical and efficient solution to this issue is the use of Google's Extract Data connector. This tool is specifically designed to facilitate the extraction of large datasets from GA4, enabling users to analyze this data externally without being hindered by GA4's standard query limits.

• Handling Large Data Volumes: The Extract Data connector is particularly adept at managing substantial amounts of data, efficiently circumventing GA4's query limits. This is crucial for businesses dealing with large-scale data analysis.

• Flexibility in Data Export: Users have the flexibility to export data to robust environments like Google BigQuery or spreadsheets. This versatility allows for deeper and more extensive analyses, tailored to the specific needs of the business.

• Customization of Data Reports: The data extractor provides the option to select specific metrics, start and end dates, and custom dimensions for reports. This level of customization ensures that the extracted data is precisely aligned with the user’s analytical objectives.

• Cost-Effectiveness: One of the key benefits of this approach is that it is free to use. This makes it an accessible option for businesses of all sizes, particularly those with limited budgets.

• Regular Data Processing: For businesses that need to process and analyze large volumes of data on a regular basis, Google’s Extract Data connector offers a reliable and efficient solution.

While Google’s Extract Data connector offers numerous advantages, there are some considerations to keep in mind:

• External Data Analysis: Once the data is extracted, it requires external tools for analysis. Users need to be proficient with tools like Google BigQuery or spreadsheet software to fully leverage the extracted data.

• Data Management and Security: Managing and securing large datasets externally can be challenging. Businesses must ensure they have the necessary infrastructure and protocols to handle and protect the data effectively.

• Time and Resource Investment: Setting up and managing data extraction processes may require a significant investment of time and resources, especially for those unfamiliar with these tools.

Google Sheets emerges as a valuable tool for overcoming data limitations in Google Analytics 4 (GA4), particularly for those managing extensive data sets. By exporting GA4 data into Google Sheets, users can bypass some of the inherent limitations of GA4's reporting capabilities. This approach provides a more flexible and adaptable environment for data analysis, allowing users to manipulate and scrutinize large data sets with greater ease and precision.

• Flexible Data Analysis Environment: Google Sheets offers a user-friendly platform for data manipulation, enabling users to apply various techniques and formulas not available within GA4. This flexibility is crucial for customized data analysis and reporting.
• Customized Reporting: The ability to customize reports according to specific requirements is a key benefit of using Google Sheets. Users can create tailored reports that align closely with their analytical objectives.
• Integration with Data Visualization Tools: Google Sheets can be seamlessly integrated with other data visualization tools, enhancing its utility for comprehensive data analysis. This integration facilitates a more holistic approach to data interpretation and decision-making.

However, while Google Sheets is a practical tool for data management from GA4, it does come with certain limitations:

• Row Limit Constraint: Google Sheets has a limitation of handling only up to 1 million cells. This constraint can be a significant issue for those dealing with larger datasets, as it restricts the volume of data that can be analyzed within a single sheet.

• Alternatives for Larger Datasets: For users managing datasets larger than what Google Sheets can accommodate, it is advisable to consider more robust alternatives. Options such as Google BigQuery or a GA-native data connector like OWOX BI offer greater capacity for data storage and analysis.

• Need for Advanced Data Analytics: While Google Sheets is effective for basic to intermediate data analysis, it may not suffice for more advanced analytics needs. Businesses requiring intricate data manipulation and analysis might find the capabilities of Google Sheets limiting and may need to explore more sophisticated data analytics solutions.

The limitations posed by the Google Analytics 4 (GA4) API request cap require alternative strategies for managing large volumes of user permissions and data. One effective solution to circumvent these limits is using a robust data connector like OWOX BI.

OWOX BI offers an efficient way to handle your analytics needs without exhausting your GA4 API quota. Collect all of your advertising data, website analytics, prepare data for reporting and get the clarity you deserve.

Furthermore, OWOX BI provides an opportunity to explore more extensive analytics capabilities and insights that can contribute to business growth. You can book a demo call with OWOX BI's product specialists to understand how their solution can be tailored to your specific business needs, helping you to navigate the challenges posed by GA4 API limitations effectively.