Hydra
lambdo
Hydra | lambdo | |
---|---|---|
1 | 3 | |
30 | 22 | |
- | - | |
0.0 | 0.0 | |
about 12 years ago | over 3 years ago | |
Haskell | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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Hydra
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Why isn't differential dataflow more popular?
I think there are a lot of similarly interesting paradigms that goes mostly unnoticed because of a lack of explanation and simple to use api's.
My personal favorite is "Functional hybrid modelling" - https://github.com/giorgidze/Hydra
lambdo
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Why isn't differential dataflow more popular?
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!
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Feature Processing in Go
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!
What are some alternatives?
blog - Some notes on things I find interesting and important.
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
timely-dataflow - A modular implementation of timely dataflow in Rust
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
tablespoon - 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
differential-datalog - DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.
openHistorian - The Open Source Time-Series Data Historian
sliding-window-aggregators - Reference implementations of sliding window aggregation algorithms