Using dbt tranformation to aggregate data during a sync?

New to Airbyte and dbt, and I’d like to transform data from sessions in a source database to aggregates in a destination during a sync.

Overly simplified, I’d like to append/upsert the results of this query from the source to the destination and run this hourly:

  DATE(created_at) AS date,
  COUNT(1) AS count
  DATE(created_at) > NOW() - INTERVAL '1 days'

The actual query is more complex, since I’d be counting the amont of times certain events occurred during each session. I already have a query that does that just fine, so I’m keeping it simple for this post.

Is this something I’d do via dbt during an Airbyte sync under the transformation section in a connection?

Looking through the guide on transformations, it looks like those run on the destination data. So I’m actually transferring all my session data (a much larger data set) to the destination first and then reducing it to aggregates?

That doesn’t quite feel like what I want to do, if I could transform the data using the source dataset instead. I also don’t want the full session data in the destination at all (maybe the transformation can clean it up afterwards?).

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You can use custom transformation to run an analytic function after every sync

I’ve seen those docs and in my scenario, that means transferring a lot of data to the destination first and then reducing it down to a much smaller aggregate dataset. I’d be transferring 1000 or more times the amount of data than I need. This seems a little inefficient, without even considering the bandwidth implications.

Is there a fundamental reason why AirByte can’t be more selective at what data is picked up at the source end before it syncs?

I know there are multiple sync modes which abstract away the query used to read in the data, but it seems like if I could provide custom logic at this stage, this would ensure only the aggregated data I want ends up being sent to the destination.

I fully understand that AirByte might just not be the kind of product I need here.

Joost, Airbyte follows ELT paradigms which moves all your data to your data warehouse. This paradigms acts like this because storage is cheap and if you need to reprocess anything you already have your data in your analytics tool.
If you’re using a source Database you can always create a view in the source and ingest data from it.