[Oliver]

Alright! You can use the UNNEST function in BigQuery, which allows you to flatten array data into a table. Here’s an example using the ‘event_params’:

`
SELECT
EP.key AS event_param_key,
COUNT(*) AS occurrences
FROM
— Replace the table name with your table
bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*,
UNNEST(event_params) AS EP
WHERE
— Replace with your date range
_TABLE_SUFFIX BETWEEN ‘20201201’ AND ‘20201202’
GROUP BY
event_param_key
ORDER BY
event_param_key ASC;
`

In this SQL query, each record in the ‘event_params’ array is transformed into a row in the ‘EP’ table, which you can then query as usual.

For more details, have a look at [this article](https://medium.com/firebase-developers/using-the-unnest-function-in-bigquery-to-analyze-event-parameters-in-analytics-fb828f890b42) which explains how to use the UNNEST function in depth when analyzing event parameters in Analytics.

[Oliver]

While it is difficult to provide a specific solution without seeing the code, this kind of inconsistency is typically associated with timing issues, such as the GA tag firing before the dataLayer is fully populated. You may want to check if your tag is set to fire when the dataLayer is fully formed, rather than at page view. It might also be worth checking the structure and integrity of the data you’re sending over to ensure that nothing is getting lost in translation. Another possibility could be data sampling in GA4, where not all events are recorded, especially if you have a high volume of traffic. It’s also important to remember that in preview mode, GTM doesn’t have the same delay in data processing as GA, so data visibility in real-time GA may be delayed. If none of these apply, reaching out to Google’s support team or forums can help you troubleshoot further.

[Oliver]

There could be a variety of reasons why your GA4 BigQuery export has stopped abruptly. Given your situation, here are a few things you could try:

1. Check the BigQuery data transfer logs: This could provide valuable insight into what went wrong. You can find this in BigQuery console under “Transfers.”

2. Validate the configuration: Make sure that the configuration has not changed. This includes the Google Analytics 4 (GA4) properties and data streams configuration in GA4 Interface, as well as GA4 data stream linkages in the BigQuery interface.

3. Manual export: Try manually exporting the data from GA4 to BigQuery for the project that’s causing issues. If it works, then there might be a problem in automated scheduling and not the export itself.

4. Check permissions: Ensure the account used to set up the export hasn’t had a change in permissions or has been disabled or deleted. If the user who started the export loses their access to the project, data export will be affected.

5. Check quotas and limits: Ensure you’re not hitting any quotas or limits that might affect your export process in BigQuery.

6. Reactivate the linkage: If all else fails, you might want to deactivate and reactivate the linkage between GA4 and BigQuery.

If these steps don’t work, it might be best to seek official Google Cloud Support assistance. However, they should provide you with enough to troubleshoot the issue.

[Oliver]

This discrepancy could be caused by a couple of things. Firstly, the API might be pulling duplicate transactions which GA4 automatically removes. Secondly, the API could just be taking longer to update. A deep look at the data and checking for duplicates will give you a better perspective.

[Oliver]

The combination of the fields event_name, event_timestamp, user_pseudo_id, and event_bundle_sequence_id are used to uniquely identify each record in the GA4 BigQuery event export table. However, it seems from your results that there are instances of multiple rows having the same values in these columns but different event parameters. What could be happening is that the event_params field, which is nested and JSON-formatted, may contain varying information across multiple events even if the identifying fields are the same. The assumption that related event_params values would always appear in one row with the same identifying record could be incorrect. There might be a mix-up of event_params data from different rows into one. A potential workaround for this could be to add a hash or fingerprint of the entire event_params object to the four mentioned fields, although there currently seems to be no surefire way of identifying unique records as of April 2023.