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  • GA4 events_ in BigQuery fragmented into multiple tables

    Posted by Zoe on 5 December 2022 at 2:54 am

    Hey folks, has anyone ever had the same issue as I’m having right now? Over the last 6 days, my BigQuery data tables linked with GA4 have oddly split into two separate tables, even though they have the same name. I’m kind of stumped! Here’s what I’m looking at right now – any idea how to handle this?

    What I’m trying to do is bring together GA4 data in BigQuery in one neat ‘table’ called events_. I’m not sure why, but only for one of my four projects, this odd splitting occurred. The other projects have more than 95 tables in events_. Here’s a picture to give you a clearer idea – does anyone know what might have gone wrong?

    Michael replied 11 months, 2 weeks ago 3 Members · 2 Replies
  • 2 Replies
  • Olivia

    28 December 2022 at 1:28 am

    It sounds like it could be a sharding issue. Google BigQuery automatically creates a new shard (or table) each day for GA4 data, and this might be why you’re seeing the data split into separate tables. To view all your data as one singular table, you should use a wildcard to query across all the “events_” tables. It should look something like this: “SELECT * FROM project.dataset.events_*“. This query will fetch the data from all the physical tables starting with “events_”. Essentially, it should unite all your sharded tables during the query itself.

  • Michael

    9 April 2023 at 2:12 am

    It’s possible that you’re running into an issue with how Google BigQuery automatically creates Partitions and Sharded tables. Every day, Google Analytics exports raw data to BigQuery and creates a new table, called a “shard.” These shards are part of the main ‘events_’ table, but they appear as separate tables when you view them in BigQuery. This could explain why your project appears to have been split into two tables even though they have the same name. If you are expecting single table, instead consider utilizing wildcard which enables you to query multiple tables at once. Remember to filter by the date, otherwise, costs could be high due to massive data. It’s always a good idea to review Google BigQuery’s documentation for common issues and best practices. If this doesn’t solve your issue, it might be worth reaching out to Google support for more in-depth troubleshooting.

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