koalas
Koalas: pandas API on Apache Spark (by databricks)
Dask
Parallel computing with task scheduling (by dask)
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| koalas | Dask | |
|---|---|---|
| 2 | 32 | |
| 3,317 | 11,965 | |
| 0.2% | 1.3% | |
| 4.6 | 9.7 | |
| about 1 month ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
koalas
Posts with mentions or reviews of koalas.
We have used some of these posts to build our list of alternatives
and similar projects.
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My new company uses Pyspark. I want to learn it before my starting date. Any advice?
If they're using databricks and you're familiar with pandas, koalas should be right up your alley .
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Spark vs Pandas
If you like excessive use of square brackets.. I mean pandas, you might wanna check out Koalas. Koalas suppose to provide pandas datafrafe API implementation atop of Spark.
Dask
Posts with mentions or reviews of Dask.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-15.
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/