What kind of aggregation queries? Can you do pre-aggregation?
Citus would probably be my pick if you want SQL.
Feel free to email.
OLAP works here, but is not great depending on how fast you need info to be available. If you're generating reports, Postgres is fine as long as your queries are properly optimized, and you can get them within minutes for massive workloads. If you need near-instant (sub 1s) results, I would recommend you sync your RDBMS to a columnar database like ClickHouse, and let the better data layout work in your favor, rather than trying to constrain a row-based DB to act like it's not.
Otherwise, both are rock solid and simple to use. I've dealt with more intensive workloads than you mentioned, with the same use-case and Postgres worked very well. ClickHouse never had a problem.
I was considering using something like Airbyte, but then I thought this may actually be complex if PG rows are updating/deleting it means I also need to sync single rows (or groups of rows) to clickhouse, and I wasn't sure how the support was for that.
It might be easier to find some "change data capture" product that will do that for you though (like Airbyte). I can't give any recommendations here, however.
[0] - https://www.peerdb.io/
Ive been researching databases now for several days straight, the choices are overwhelming but I've pretty much narrowed my use case down to an RDBMS system.
I need to essentially handle 100's of millions of "leads" (and 10s of millions per day) which can make up any number of user fields. over 1B total
I need to resolve duplicate leads either in realtime or near realtime. A duplication can occur across a combination of 1 or more fields, so basically OLTP type operations (select, update, delete on single rows)
I do need to run large OLAP queries as well across all data
I've looked at things like scylla and whatnot but they seem too heavy duty for my volume. it's not like i need to store trillions of messages like discord in some huge event log.
I was considering these 3 options...
1. planetscale
2. citus
3. cockroachdb
I havent really narrowed it down further than this, but i liked the idea of still having RDBMS features without needing to worry about storage and scaling with just sheer write volume.
It seemed i could then do my basic OLTP stuff that i need, and citus had a cool demo how some OLAP query on 1B rows ran in 20s with 10 nodes, and that also fits a reasonable time for queries (BI tools will be used for that)