arquero VS arrow-julia

Compare arquero vs arrow-julia and see what are their differences.

arquero

Query processing and transformation of array-backed data tables. (by uwdata)

arrow-julia

Official Julia implementation of Apache Arrow (by apache)
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arquero arrow-julia
8 4
1,198 277
2.3% 0.7%
4.6 5.6
about 2 months ago 13 days ago
JavaScript Julia
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
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.

arquero

Posts with mentions or reviews of arquero. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-07.
  • Show HN: Matrices – explore, visualize, and share large datasets
    2 projects | news.ycombinator.com | 7 Dec 2023
    Hey HN, I'm excited to share a new side project I've been working on.

    The product is called Matrices. You can check it out here: https://matrices.com/.

    With Matrices, you can *explore*, *visualize*, and *share* large (100k rows) datasets–all without code. Filter data down to just what you want, visualize it with built-in charts, and share your results with one click.

    You can use it today (no login or waitlist or anything). Just copy and paste your data from a google sheet or CSV file.

    It's hard to describe the feeling of "gliding over data" you get with Matrices, so I'd rather *show* you how it works instead. This 75s video will give you a sense of how it works: https://www.youtube.com/watch?v=Rrh9_I3Ux8E.

    Data is stored locally in your browser until you publish it, though small sample does go to the OpenAI APIs for AI-assisted features.

    I started building Matrices because I wanted a tool that made it easy to explore new datasets. When I'm first trying to dig into data, I'll have one question... that leads to another... that will invariably lead to five more questions. It's sort of a fractal process, and I couldn't find many good options that were fast, responsive, and visual.

    I figured this crowd would be interested in tech stack as well, it's using arquero [1] bindings over apache arrow for in-memory analytics, and visx [2] for visualizations. I'd like to add duckdb-wasm support at some point to open up a wider set of databases. Data is serialized as parquet to save a bit on bandwidth + storage.

    Give it a spin, and let me know what you think. This is my first 'serious frontend project' so I appreciate any and all feedback and bug reports. Feel free to comment here (I'll be around most of the day), or shoot me a note: [email protected]

    [1]: https://uwdata.github.io/arquero/

  • Goodbye, Node.js Buffer
    15 projects | news.ycombinator.com | 24 Oct 2023
    https://github.com/uwdata/arquero
  • Arquero is a JavaScript library for query processing and transformation of array-backed data tables
    1 project | /r/programming | 24 Jul 2022
  • Arquero – data tables wrangling in JavaScript
    1 project | news.ycombinator.com | 22 Jul 2022
  • Hal9: Data Science with JavaScript
    4 projects | /r/datascience | 9 Sep 2021
    Transformations: We found out that JavaScript in combination with D3.js has a pretty decent set of data transformation functions; however, it comes nowhere near to Pandas or dplyr. We found out about Tidy.js quite early, loved it, and adopted it. The combination of Tidy.js and D3.js and Plot.js is absolutely amazing for visualizations and data wrangling with small datasets, say 10-100K rows. We were very happy with this for a while; however, once you move away from visualizations into real-world data analysis, we found out 100K rows restrictive, which gets worse when having 100 or 1K columns. So we switched gears and started using Arquero.js, which happens to be columnar and enabled us to process +1M rows in the browser, descent size for real-world data analysis.
  • Arquero – Query processing and transformation of array-backed data tables
    1 project | news.ycombinator.com | 16 Feb 2021
  • Apache Arrow 3.0.0 Release
    10 projects | news.ycombinator.com | 3 Feb 2021
    Take a look at the arquero library from a research group at University of Washington (the same group that D3 came out of). https://github.com/uwdata/arquero

arrow-julia

Posts with mentions or reviews of arrow-julia. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-18.
  • Julia 1.8 has been released
    8 projects | news.ycombinator.com | 18 Aug 2022
    For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
  • How to adapt Arrow.Table columns (naturally per record batch basis) into CuArrays for GPU processing?
    1 project | /r/Julia | 2 Mar 2022
  • Reading HDF5 Files
    2 projects | /r/Julia | 9 Mar 2021
    I guess current preferred format not feather, but arrow: https://github.com/JuliaData/Arrow.jl
  • Apache Arrow 3.0.0 Release
    10 projects | news.ycombinator.com | 3 Feb 2021
    Excited to see this release's official inclusion of the pure Julia Arrow implementation [1]!

    It's so cool to be able mmap Arrow memory and natively manipulate it from within Julia with virtually no performance overhead. Since the Julia compiler can specialize on the layout of Arrow-backed types at runtime (just as it can with any other type), the notion of needing to build/work with a separate "compiler for fast UDFs" is rendered obsolete.

    It feels pretty magical when two tools like this compose so well without either being designed with the other in mind - a testament to the thoughtful design of both :) mad props to Jacob Quinn for spearheading the effort to revive/restart Arrow.jl and get the package into this release.

    [1] https://github.com/JuliaData/Arrow.jl

What are some alternatives?

When comparing arquero and arrow-julia you can also consider the following projects:

perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to: https://github.com/hal9ai/hal9]

ClickHouse - ClickHouse® is a free analytics DBMS for big data

regression-js - Curve Fitting in JavaScript.

TableIO.jl - A glue package for reading and writing tabular data. It aims to provide a uniform api for reading and writing tabular data from and to multiple sources.

cylon - Cylon is a fast, scalable, distributed memory, parallel runtime with a Pandas like DataFrame.

vega-loader-arrow - Data loader for the Apache Arrow format.

go-py-arrow-bridge - Bridge between Go and Python to facilitate zero-copy using Apache Arrow