Bigquery location consistency over multiple multi regions datasets

[Alex10]

My organisation has multiple datasets in different projects with US multi-regions setting. I never had any issues to join tables from those multiple datasets.

However, I am wondering how is it possible as BQ is supposed to be able to join only tables in the same location and multi-regions doesnt seem to have any system to guarantee co-location of dataset in the same project or organisation.
Am i missing something?

I am wondering about this because i have to deploy some data tools of my own and i wanted optimize their location and vpc settings.

[ms4446]

You're right that BigQuery typically requires tables to be in the same location for joining. However, in the context of US multi-region datasets, BigQuery's handling of data is a bit different. In a multi-region setting like the US, BigQuery manages data replication across various data centers within that multi-region. This replication is handled internally by BigQuery and is transparent to users.

Therefore, when you have datasets in a US multi-region setting, BigQuery treats them as if they were in the same logical location, even though they are physically distributed across multiple regions. This allows for joining tables from these datasets seamlessly, as BigQuery internally manages any necessary data movement and optimization.

When it comes to deploying your data tools, the location of your data and the nature of your queries are important considerations. If your operations are confined to a single region, placing your data tools in the same region can enhance performance due to reduced data latency. On the other hand, if your operations involve frequent interactions across multiple regions within the US, utilizing BigQuery's capabilities in a multi-region setting can offer efficient and seamless data processing.

Keep in mind that while BigQuery's multi-region setup optimizes for data availability and querying across regions, it's still crucial to consider aspects like compliance with data residency laws and the cost implications of data storage and access patterns.