cloudquery VS polars

Check out CloudQuery - https://github.com/cloudquery/cloudquery for an easy cloud asset inventory.

There both do something like what you're looking for.... https://github.com/cloudquery/cloudquery https://github.com/openraven/magpie

Unified API is a holly grail but as many said quite difficult to abstract every use case in a scalable way that won't break. At CloudQuery (https://github.com/cloudquery/cloudquery) we focus solely on the ELT use-case(Founder/Maintainer here).

Congrats!! How does it compare to the ELT space and the modern data stack where you have ingestion/storage/visualization layers decoupled?

Asking as the founder of CloudQuery (https://github.com/cloudquery/cloudquery), Saw Datasette quite a few times around data exploration but curious to hear about the most popular use-cases of Datasette!

Congrats!! We also focus on performance at CloudQuery (https://github.com/cloudquery/cloudquery) by using Golang, gRPC and still trying to be abstract enough to support different databases :)

In any case good luck!

CloudQuery for ETL

Another ELT framework that's an alternative to Airbyte

Another alternate ELT

Check out CloudQuery, we have PostgreSQL source connectors and S3 destination that supports parquet (Disclaimer: Maintainer and founder here)

Hello! Im Yevgeny, Founder & maintainer at CloudQuery . We've built CloudQuery as an open source high performance ELT framework so you should get pretty good results syncing all your cloud assets from high number of accounts (we have users syncing more than 10K Azure subscription and thousands of AWS accounts concurrently).

This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.

I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).

https://github.com/pola-rs/polars/releases/tag/py-0.19.0

That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection: