Question 1

Which side needs cloud storage configured?

Accepted Answer

Only MotherDuck. Writing into MotherDuck stages Parquet files in an S3 bucket you supply and then loads them, so that bucket plus AWS credentials are part of the MotherDuck connection. BigQuery submits a load job directly and needs no customer bucket, which makes this pairing lighter to set up than pairing MotherDuck with Redshift or Databricks, where both sides want staging.

Question 2

How do BigQuery STRUCT and ARRAY columns map into DuckDB?

Accepted Answer

Onto DuckDB structured types, so a nested record or repeated field arrives as a STRUCT or LIST you can query directly in MotherDuck. Both engines model nested data natively, which is why this pair holds up better than moving BigQuery nested data into a warehouse that would force it into a JSON string column.

Question 3

Can I sync BigQuery into MotherDuck incrementally?

Accepted Answer

Yes, using a tracking column such as an updated timestamp, with the first run doing a full read and later runs resuming from the saved position. That matters more with BigQuery than with most sources because BigQuery bills on bytes scanned, so an incremental sync is not just faster but materially cheaper than repeatedly rereading a large table.

Question 4

Can MotherDuck write back into BigQuery?

Accepted Answer

Yes. MotherDuck is a full source as well as a destination, and BigQuery accepts create, update, create or update, and replace modes with tables created and columns migrated as your source changes. One thing to know is that MotherDuck views are excluded from bulk sync object discovery, so if the output you want to publish to BigQuery is defined as a view, build a model over it instead.

Connect MotherDuck with BigQuery

Connect MotherDuck and BigQuery with Polytomic

Storage on one side only

Nested and repeated fields kept

Incremental rather than reload

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Connect BigQuery in Polytomic

Create a sync between MotherDuck and BigQuery

Map fields and enable the sync

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