Integrating GA4 with BigQuery for Streamlined Data Extraction

[Isabella]

Sure, you can achieve this by querying multiple days across the different tables. It’s simply a matter of adjusting the ‘_table_suffix’ in your SQL code to the date range you’re interested in.

Here’s a quick example that might help:

`
select
*
from
— replace this with your Google Analytics 4 export location in BigQuery
ga4bigquery.analytics_(change_to_yours).events_*
where
_table_suffix between ‘20230101’ and ‘20230327’
`

The ‘_table_suffix’ represents the dates, so when you specify ‘between 20230101 and 20230327’, you’re asking for data from all of the tables (or days) within that range.

For more details, check this [tutorial](https://www.ga4bigquery.com/tutorial-how-to-query-multiple-analytics-events-tables-with-table-suffix-ga4/). Quite handy!

[Jayden]

You are correct in noticing that Google Analytics 4 (GA4) automatically creates a separate table for each day when exporting data to BigQuery. Unfortunately, there is currently no direct way to change this setup within GA4 to create a single table with data streaming in real-time.

However, there is a solution provided by BigQuery itself, using a feature called “Partitioned Tables”. A partitioned table in BigQuery is essentially a single, large table divided into smaller, manageable pieces called partitions. You can define a partition in various ways, but in your case, you might want to use a time-unit column, such as the timestamp or date. Then you run your queries only on the relevant partitions of data, effectively treating it like one single table.

This approach can offer faster query performance and lower cost, as you’re only using the data you need. You would need to regularly maintain this partitioned table to ensure new data is integrated.

Unfortunately, this does add an extra layer of complexity as BigQuery will need to be managed outside of GA4 for this, but this is currently the best solution available as of this moment for streamlining GA4 data into a single table in BigQuery.

To note, GA4 designs its data export this way (i.e., dividing data into daily tables) to lower costs. Since queries in BigQuery are charged based on the amount of data they process, by querying data from a specific table (which equates to a specific day’s data), you are only charged for the processing of that particular day’s data. So, this approach can be cost-effective if you often need to analyze data on a day-by-day basis.