fugue VS RasgoQL

Please integrate it with Fugue.

https://github.com/fugue-project/fugue

Fugue is an interesting library in this space , though I haven’t tried it

https://github.com/fugue-project/fugue

A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.

fugue — distributed computing done easy

This looks similar to fugue, am I right? How do they compare?

I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.

There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.

I built an open source package to bridge the gap between python and dbt, would love your feedback if you have a chance to check it out: https://github.com/rasgointelligence/RasgoQL

I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.

I created RasgoQL to give anyone a pandas-like syntax that you can use to quickly generate hundreds of lines of SQL that will run directly in your Snowflake or BigQuery data warehouse (with more data warehouse support coming soon). The best part? In one line of code, you can export this SQL to your dbt project so that it can run in production alongside other data pipelines.

You can check it out here: https://github.com/rasgointelligence/RasgoQL