[GA4] BigQuery Export: 8 Costly BigQuery Mistakes and How to Prevent Them

Exporting your GA4 data to BigQuery can be a powerful way to manage and analyze a great chunk of business data. However, if not done right, it can quickly lead to soaring costs and inefficiencies. 

In this article, we’ll highlight some common mistakes professionals make with GA4 BigQuery exports and provide actionable tips to avoid them, helping you optimize performance while keeping costs in check.

This article is Part 6 in our series, "[GA4] BigQuery Export.

If you're following along, check out the previous articles in the series:
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• Part 1: How to Connect GA4 to BigQuery Step-by-Step
• Part 2: Events Table Schema and Managing Dates
• Part 3: How to Query GA4 Event Data
• Part 4: How to Unnest GA4 Event Parameters
• Part 5: How to Extract GA4 User Properties and Metrics

‍Continue your journey with the next one:

• Part 7: How to Build Your First Dashboard Using Looker Studio

Why Exporting Your GA4 Data to BigQuery is Important for Data Analysts

Exporting GA4 data to BigQuery gives data analysts more control and flexibility over their analytics, allowing them to retain and analyze data beyond GA4’s default retention limits. 

Here are some key reasons for exporting GA4 data to BigQuery:

• Long-term Data Retention: Unlike GA4, which limits data retention, BigQuery allows you to store data indefinitely, ensuring no loss of historical data.
• Cost-Effective: With Google Cloud’s free monthly allowance, the cost of storing data is minimal. For example, handling data for 100,000 users a day could cost as little as $2 per day.
• Data Cleansing: BigQuery allows you to clean and correct errors in your GA4 data, such as fixing UTM parameter mistakes, and aligning campaign data with your marketing strategies.
• Advanced User Journey Analysis: BigQuery lets you analyze user behavior on a deeper level, offering insights into key user journeys and conversion paths.
• Unlimited Conversions: You can create and test as many conversions as needed, even applying them retroactively – something that GA4 doesn’t allow.
• Data Integration: BigQuery supports combining your GA4 data with other business data (e.g., CRM, advertising revenue), giving a more comprehensive view of KPIs and performance metrics.
• Faster Reporting: Integrating BigQuery with BI tools like Looker Studio or Google Sheets allows for faster and more dynamic reporting, eliminating the delays often experienced with GA4’s native reporting tools.
• Advanced Analytics and Machine Learning: BigQuery enables the use of tools like Python, R, and BigQuery ML, allowing teams to perform advanced analytics.

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8 Costly GA4 to BigQuery Export Mistakes and Their Solutions

Exporting GA4 data to BigQuery is essential for advanced data analysis, but many teams encounter costly mistakes during the process. This section outlines eight common GA4-to-BigQuery export mistakes and provides practical solutions to avoid them, helping optimize costs and performance.

Mistake 1: Not Setting Budget Limits on Data Processing

One of the most common yet costly mistakes when managing GA4 data export to BigQuery is failing to set budget limits on data processing.

What is the mistake?

Failing to set budget limits for data processing in Google Cloud Platform (GCP) projects can lead to unexpected and often excessive charges. Without clear budget controls, BigQuery costs can spiral out of control, especially when processing large datasets from GA4 exports.

Why does it happen?

This issue often arises because users may not be familiar with the budget management tools available in GCP. Additionally, some teams might underestimate the scale of data being processed and overlook the importance of setting cost limits and alerts.

When does it happen?

This problem typically surfaces when managing high-volume data exports from GA4 to BigQuery, especially when queries are run frequently or without proper optimization. Without budget controls, costs can accumulate quickly, unnoticed until they appear on a billing statement.

How should it be fixed?

Set budget limits and configure cost alerts in GCP to monitor your spending to avoid this issue. These tools provide real-time notifications when you approach your budget threshold, helping prevent unexpected expenses. You can also export billing data to monitor usage closely.

💡 While setting budget limits in GCP is crucial to prevent unexpected data processing costs, it’s only one aspect of managing BigQuery expenses. To gain deeper insights into how BigQuery pricing works and strategies to control costs, check out our comprehensive guide on mastering BigQuery pricing.

Dive deeper with this read

Understanding BigQuery Pricing: A Complete Cost Prediction Guide

Mistake 2: Using SELECT * to Extract All Data

One of the most common and costly mistakes in BigQuery is using SELECT *, which retrieves all columns from a dataset, even if only a few are required. 

The problem in detail

When you use SELECT *, BigQuery reads all the columns in a table, even if your analysis only needs a subset of them. The more data BigQuery reads, the more you’re billed, regardless of how much data is ultimately returned. This practice inflates costs unnecessarily, particularly when dealing with large tables or frequent queries.

Why it happens

This mistake often happens because it’s faster and easier to write SELECT * than to manually specify each column. Many users, especially those under time pressure, may overlook how much unnecessary data they are processing. 

When it occurs

This issue tends to surface during exploratory data analysis, quick reporting tasks, or when users are unfamiliar with the exact structure of the dataset. It’s particularly common in large datasets where users want to avoid manually selecting columns. 

How to correct it

To avoid this, always specify only the columns you need for your query. If you require most columns but want to exclude a few, use SELECT * EXCEPT to remove unnecessary ones. This approach minimizes the data processed and helps reduce query costs. 

Additionally, in the BigQuery console, you can use the Query Validator to check the approximate data volume that will be processed by your query. This tool provides an estimate in real time, allowing you to make adjustments before running the query. 

Mistake 3: Incorrect Date Range Selection

Using inaccurate or overly broad date ranges in your queries can result in processing far more data than necessary, leading to inflated costs and slower query performance.

What’s the misstep?

Querying without specifying the correct date range or using overly broad ranges causes BigQuery to process unnecessary data. This not only increases query time but also impacts the budget, as you’re billed based on the amount of data processed each time.

Underlying cause

This happens when users forget to narrow their queries to relevant dates, or they aren’t clear about the specific timeframes they need. Sometimes, users query entire datasets by default, unaware of the significant impact this can have on cost and performance when processing historical data.

When It becomes a problem

The issue is most noticeable when querying large, historical datasets without applying a relevant date filter. Over time, as GA4 data accumulates, querying everything without a focused time range can quickly drive up costs and slow down reporting.

The solution

To avoid this, always include accurate date filters in your queries, using specific functions like WHERE event_date BETWEEN to limit the data being processed. The BigQuery console’s query validator helps estimate the data volume, allowing you to optimize your queries before running them​. Reviewing past queries for missed date filters can also help reduce future costs.

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Mistake 4: Unnecessary Event Parameters and User Properties Usage

Tracking too many event parameters and user properties can lead to a bloated dataset, significantly increasing storage and query processing costs in BigQuery.

Where it goes wrong

Overloading your GA4 exports with excessive event parameters and user properties results in larger datasets that are costlier to store and process. Each unnecessary parameter or property adds weight to your queries, driving up costs without providing meaningful insights.

Why teams fall into this trap

Many teams take a “collect everything” approach, assuming that every piece of data could be useful later. However, they often neglect to perform regular audits, causing unused parameters to accumulate and bloat the dataset. This adds unnecessary complexity to future analyses while raising both storage and processing costs.

When it happens

This issue typically occurs over time as event parameters and user properties are added without thoughtful cleanup. Teams often forget to remove old or irrelevant parameters, allowing their dataset to grow larger than necessary, particularly when new events are implemented, but older ones remain in place.

Steps to resolve it

To address this, 

• Regularly audit your event parameters and user properties. 
• Clean up and remove unused or irrelevant parameters to keep your dataset lean and manageable. 
• Make sure to review your tracking setup periodically to prevent unnecessary parameters from being collected in the first place​.

Doing this will not only reduce storage and processing costs but also improve the efficiency of your BigQuery queries. 

💡 While auditing event parameters and user properties is key to reducing unnecessary data bloat, optimizing your dataset can go even further. To fully understand how to extract valuable insights from GA4 user properties and metrics, explore our detailed guide on utilizing user data in BigQuery.

Dive deeper with this read

[GA4] BigQuery Export: How to Extract GA4 User Properties and Metrics

Mistake 5: Tracking Too Many Custom Events in GA4

Tracking excessive events in GA4 can cause your property to hit daily event limits, leading to higher processing costs and reduced data quality.

What’s the error?

Attempting to track too many events across your site or app can lead to hitting GA4's export limit of 1 million events per day. This overload not only impacts performance but also results in higher BigQuery processing costs due to the larger volume of data.

What leads to this?

Users often try to track every possible user interaction, without prioritizing critical events. This results in an inefficient tracking setup, collecting redundant data, and straining both GA4 and BigQuery. Over-tracking can overwhelm your analytics system, making it harder to extract meaningful insights from the data.

When it becomes a problem

This issue typically arises when the number of events exceeds GA4’s limit of 1 million events per day. Exceeding this cap can lead to pausing daily exports to BigQuery, creating gaps in your data and causing additional storage and processing costs as you handle large datasets inefficiently​.

Solution path

To resolve this, focus on tracking only key funnel events and high-value user interactions. Regularly review and optimize your event setup to avoid unnecessary tracking. You can also consider using tools like OWOX BI Streaming, which helps manage large datasets more efficiently, preventing you from hitting GA4’s event limits and maintaining smooth data exports.

Mistake 6: Connecting Raw Data to Looker Studio Dashboards

Connecting raw, unoptimized data to Looker Studio dashboards often results in poor performance and inflated BigQuery costs due to inefficient data queries.

Where users go wrong

When users connect raw, unfiltered data directly to Looker Studio, it leads to slow dashboard performance. Raw data is typically large and complex, which means every query run in Looker Studio will pull massive amounts of data, significantly increasing BigQuery processing costs.

Why this happens

This issue occurs because raw datasets are typically unaggregated and contain a large number of fields. Many users attempt to create real-time dashboards without first optimizing or summarizing the data. Since Looker Studio runs live queries against BigQuery, these inefficient queries cause delays and result in high costs.

When it’s an issue

This mistake is common when teams try to build dashboards without processing data through data marts or aggregating data beforehand. Connecting unprocessed data for real-time visualizations in Looker Studio can quickly lead to sluggish dashboards and excessive BigQuery usage, especially for large datasets​.

What to do Instead

Instead of connecting raw data directly, use data marts to aggregate and summarize your data before it reaches Looker Studio. Alternatively, tools like the OWOX Reports can help streamline data into Google Sheets, which can then be used as a source for Looker Studio. This allows for faster, more efficient dashboarding while significantly reducing costs and improving performance​.

Mistake 7: Too Frequent Data Refreshes

Frequent data refreshes can lead to higher BigQuery processing costs without significantly improving data relevance or accuracy.

What causes it

Teams often set too frequent refresh intervals, either due to a misunderstanding of how often data needs to be updated or a desire for real-time insights. This results in unnecessary data processing and higher costs, especially when dealing with large datasets.

Why does it happen?

Frequent data refreshes often occur due to a desire for real-time data or a lack of knowledge about how often updates are necessary. Many users think that refreshing data too often will lead to more accurate or timely insights, overlooking the fact that refreshing data too often adds little value.

This is especially common when teams default to shorter intervals without assessing the actual business need, leading to unnecessary processing costs.

When do hits become a problem?

This issue surfaces when data is refreshed more often than necessary. Each refresh pulls and processes a large volume of data, which can become costly if done multiple times a day without adding significant value in terms of timely insights or data accuracy.

The fix in terms of cost

To control costs, set a refresh frequency that matches your business needs. For many use cases, daily or weekly updates provide enough accuracy without incurring unnecessary processing costs. 

Additionally, using materialized views can significantly reduce both query costs and refresh times. By caching pre-calculated query results, materialized views allow subsequent queries to use this stored data rather than reprocessing the entire dataset, which reduces the volume of data processed.

Mistake 8: Failing to Use Partitioned Tables

Not using partitioned tables in BigQuery leads to inefficient queries, where the entire dataset is processed even when only a subset of data is needed, resulting in higher costs and slower performance.

Identify the error

The error comes from querying large, unpartitioned datasets, which forces BigQuery to scan all available data, even if only a small portion is relevant. This significantly increases processing costs and query times.

Understand why It occurs

Teams often neglect partitioning because they are either unaware of the benefits or find it easier to query the entire dataset without organizing it into partitions. This lack of partitioning can also stem from not fully understanding how to set up partitions based on time or other criteria.

Determine when it happens

This issue typically arises when querying large datasets over extended periods without filtering by relevant date ranges or criteria. Without partitions, queries process more data than necessary, driving up costs and affecting performance.

Find out the correct fix

To resolve this, use partitioned tables to divide your data based on date or other relevant criteria, such as a customer ID or geographic region. Partitioning reduces the amount of data processed in each query, leading to lower costs and faster query execution times. Additionally, clustering your tables can further optimize query performance by organizing data based on specific columns.​

💡While using partitioned tables can drastically reduce costs and improve query performance in BigQuery, there’s more to optimizing your data strategy. To learn how to set up and maximize the benefits of partitioned tables, explore our in-depth guide on partitioning in BigQuery.

Dive deeper with this read

Partitioned Tables in BigQuery: Everything You Need to Know

Improve Data Handling with BigQuery Functions

BigQuery provides powerful functions to help users efficiently manage, process, and analyze large datasets. These functions simplify complex operations, improve query performance, and enable seamless data transformation, making data analysis more effective and scalable.

• String Functions: Modify and manipulate text data using functions like CONCAT, SUBSTR, and REPLACE to merge, extract, and clean strings for analysis.
• Aggregate Functions: Perform calculations across multiple rows using SUM, AVG, COUNT, and MAX to summarize and analyze large datasets efficiently.
• DML (Data Manipulation Language): Insert, update, delete, and merge records in BigQuery tables using INSERT, UPDATE, DELETE, and MERGE to manage real-time data changes.
• Window Functions: Execute row-level calculations without collapsing results using ROW_NUMBER, RANK, LEAD, and LAG to analyze sequential data.
• Date Functions: Handle date-based calculations with functions like DATE_ADD, DATE_DIFF, and DATE_TRUNC to manipulate and extract date-related insights.
• Conversion Functions: Convert data types using CAST and SAFE_CAST to ensure compatibility when working with numbers, text, and date formats.

Optimize Your Data Management with OWOX Reports Extension for Google Sheets

OWOX Reports offers powerful tools to streamline and optimize data management, especially for businesses exporting GA4 data to BigQuery. By automating processes like data collection and transformation, OWOX Reports reduces the time spent on manual reporting tasks, allowing teams to focus on extracting insights and making strategic decisions.

In addition to simplifying workflows, OWOX Reports integrates seamlessly with BigQuery, enabling data consolidation from various sources into a single, unified platform. This improves data accuracy and enhances the depth of analysis, leading to more actionable insights.