connector-x VS Rudderstack

This was originally a blocker, however, we managed to set up a multi-stage Docker build to build from source. Here is the Github issue where we, along with community members, managed to solve it.

There's packages like connector-x and polars that do a lot of what you're mentioning out of the box. I used these two to massively speed up an SQLalchemy + Pandas based ETL in the past as well.

Thanks for sharing connector-x, I will also start to use it. I wonder if there are a list of tools like that. I know Ruff, Polars, pydantic-core.

I was trying https://github.com/sfu-db/connector-x and hacking around with this https://github.com/spitz-dan-l/postgres-binary-parser but it turned out that a COPY to csv using asyncpg and then converting to parquet was the fastest.

if you store the OHLC data in a relational database, use connector-x to load the data into pandas dataframe

For SQL reading I'd really recommend connector-x, they do a great job preventing unneeded serialization and don't have to go through python.

Indeed, currently we do not support persistent connections among different queries. We target more on the bulk loading scenario where the bottleneck is caused by the data size and the connection construction overhead is negligible. However, one possible solution to the problem is to expose our connection pool object that we use inside Rust to users, so the next call could reuse the same pool. We do not plan for this yet, but happy to see whether this is a common need! Feel free to open an issue in our github repo: https://github.com/sfu-db/connector-x

Feel free to ask any questions here or open an issue in our github repo: https://github.com/sfu-db/connector-x . You can also join our discord community: https://discord.com/invite/xwbkFNk and ask question under connector channel!

If CDP(such as RudderStack) were a restaurant, then Data Integration is the guy that gets all raw ingrediants from different shops and makes it available to Chef that sorts and combines raw ingrediants to make a dish. The chef can't cook anything without raw ingrediamt. Similarly Data Integration is also an important component in CDP that collects customer data from various sources and them other components unify it and activate it.

More details on GitHub repository - https://github.com/rudderlabs/rudder-server

Are you RudderStack?

In bigger setups, all you want is a data collector and router so that you can feed the data into multiple destinations, depending on the use case. Analytics is just one. Example: https://www.rudderstack.com/ & https://www.rudderstack.com/replace-google-analytics-4-guide...

Check out RudderStack, a Go project to build data pipeline. Our slack is quite active. The best way to contribute is by creating a new integration with your favorite tool. You do not need to rely to too much on existing knowledge about inner workings of the project to do so, so it is beginner friendly.

Interesting. About "Redshift need google sheet sync to table", wouldn't this be more aligned with the responsibility of a CDP(such as RudderStack) as opposed to something we expext a warehouse to do?

The challenge is over. Winners have been announced. When we are ready for the next one, will announce on RudderStack GitHub repo

Super. This is amazing. Sharing your project with the community. If you get a chance, try out RudderStack to build your pipeline.