lambdo VS rslint

It will return the sum of all values in column A. For large tables it will take some time to compute the result. Now assume we append a new record and want to get the new result. The traditional approach is execute this query again. A better approach is to process this new record only by adding its value in A to the result of the previous query. It is important in (stateful) stream processing.

Something similar is implemented in these libraries which however rely on a different data processing conception (alternative to map-reduce):

https://github.com/asavinov/prosto - Functions matter! No join-groupby, No map-reduce.

https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.

Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:

    https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

Author here. A few other Rust projects to note that I didn't mention in the original post I've since found:

- Boa (JS engine in Rust) – https://github.com/boa-dev/boa

- RSLint (JS/TS linter in Rust) – https://github.com/rslint/rslint

- Node version manager in Rust – https://github.com/Schniz/fnm

If you know of any other popular ones, let me know. I'm keeping a list :)

[4]: https://github.com/rslint/rslint