Following are the main advantages of using Google BigQuery for Google Analytics:

#1 BigQuery is specially designed for storing and querying big data (very large data sets, usually in terabytes). 

#2 Since BigQuery is a data warehouse, it is an excellent tool for combining data from several data sources (like Google Analytics, Google Ads, Facebook etc) for the purpose of advanced reporting and analysis.

Thus the use of BigQuery helps in fixing data integration issues and enables robust cross-device and cross-platform data analysis and reporting.

#3 You can access raw hit level Google Analytics data in BigQuery which is not possible when you use the Google Analytics user interface. 

#4 You can access 100% unsampled data even if you are not a GA360 customer.

#5 You can manipulate Google Analytics data in a way which is many times simply not possible by using the Google Analytics user interface. 

For example, certain dimensions and metrics combinations can not be used/queried together whether you use the Google Analytics user interface or Google Analytics API. BigQuery has no such limitations. 

For example, you can easily combine session-based dimensions (like source/medium) with user-level or page-level dimensions and metrics in BigQuery.

This is one of the biggest advantages of using BigQuery. It makes advanced data segmentation and analysis possible. It removes most of the limitations which come when you use the GA user interface or API for querying analytics data. 

There are companies/analysts out there who use BigQuery a lot more than the GA user interfaces for querying data. 

#6 There are many limitations when you query user-level data via the GA user interface.

For example, you can not query Goal Conversion rate data by users. But there are no such limitations with BigQuery. You can easily query user-level data in BigQuery. 

#7 When you use the GA user interface, the goals and view filters do not work retroactively. The goal and filter data is collected and reported only from the date you first set up your tracking.

So if you want to calculate say goal conversion rate based on historical data then it is not possible. But BigQuery has no such limitations. You can calculate both goal completions and goal conversion rate based on historical data. 

#8 BigQuery allows you to easily filter out or modify incorrect data from your analysis and reports. But when you use GA user interface to query data, you can not easily filter out incorrect data and you can not modify incorrect data. Data once skewed is skewed for good in the GA user interface.

#9 You can easily integrate BigQuery with data visualization tools like Google Data Studio and can thus easily visualize Google Analytics data.

#10 When you use BigQuery you can easily store and re-run queries thus saving a lot of time in data retrieval. 

#11 BigQuery allows you to query even terabytes of data within seconds. Since BigQuery can execute SQL statements very fast, it can be used for realtime analytics. 

#12 You can store a massive amount of data in BigQuery for relatively low prices.

#13 BigQuery is actually easy to learn and use thought it may look quite scary if you have never used it before.

#14 You don’t need to install or set up anything in order to use BigQuery. You can be up and running in a few minutes/hours. 

#15 BigQuery is easy to use with multiple users and teams. 

Other articles on Google Analytics BigQuery

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3. What is Google BigQuery Sandbox and how to use it
4. Understanding the BigQuery User Interface
5. Sending data from Google Analytics to BigQuery without 360
6.  How to connect GA4 (Google Analytics 4) with BigQuery
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8. Using Google Cloud pricing calculator for BigQuery
9. How to access BigQuery Public Data Sets
10. How to use Google Analytics sample dataset for BigQuery
11. Connect and transfer data from Google Sheets to BigQuery
12. How to query Google Analytics data in BigQuery
13. How to send data from Google Ads to BigQuery
14. What is BigQuery Data Transfer Service & how it works.
15. How to send data from Facebook ads to BigQuery
16. How to send data from Google Search Console to BigQuery
17. How to pull custom data from Google Analytics to BigQuery
18. Best Supermetrics Alternative – Dataddo
19. Google Analytics BigQuery Tutorial
20. How to backfill Google Analytics data in BigQuery
21. How to connect and export data from GA4 to BigQuery