1. > However, customers still get a lot of value out keeping downstream systems synced with their warehouse tables. For instance, you can define which people you want to receive different campaigns and make sure that's consistent across all your ad platforms.
Ah thank you that's a very helpful example for understanding your product.
2. > Some people have warehouse tables with many billions of rows.
I don't doubt customers have multi billion row tables in their CDWs but I guess I'm not seeing why you would need to send billions of rows to FB (or any other downstream system that isn't an OLAP database) rather than some much smaller payload distilled from that data via an ML model or SQL query. I admittedly have never run a FB ad campaign but Meta has 3.35 billion daily active users [1] across all of their products. If they are sending billions of rows to FB, do your customers have individualized ad campaigns for every single FB user? Perhaps I'm just not familiar enough with the state of modern digital advertising.
3. > If you have a billion rows, you need to hit a very high rows per second number in order to run a sync in a feasible amount of time.
I wonder why do you have to send billions of rows to FB every time? Surely you send it once for initial setup and then incrementally sync smaller deltas? And presumably your customer is OK with the initial setup being slower. Unless your customer is doing billions of writes to their CDW in between syncs?
Thanks for taking the time to explain!
[1] https://s21.q4cdn.com/399680738/files/doc_financials/2024/q4... page 10
You're right that typically the day to day delta we need to sync is much smaller. However, customers often want to change something for a large fraction of their dataset, requiring a large update. Also we support a our Personalization API product, for which customers do frequently want to refresh what personalizations they're showing to all of their users. For this we do need to be regularly syncing all the data.