perspective VS Apache Arrow

Compare perspective vs Apache Arrow and see what are their differences.


A data visualization and analytics component, especially well-suited for large and/or streaming datasets. (by finos)

Apache Arrow

Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing (by apache)
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perspective Apache Arrow
18 25
3,959 8,718
3.4% 1.7%
9.6 9.9
about 23 hours ago 7 days ago
C++ C++
Apache License 2.0 Apache License 2.0
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.


Posts with mentions or reviews of perspective. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-28.

Apache Arrow

Posts with mentions or reviews of Apache Arrow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-20.
  • pigeon-rs: Open source email automation written in Rust
    5 projects | | 20 Nov 2021
    Connectorx is using arrow2 data format for fetching from a database. This data format is optimized for columnar data [1]:
  • Introducing tidypolars - a Python data frame package for R tidyverse users
    9 projects | | 10 Nov 2021
    I think having a basic understanding of pandas, given how broadly it's used, is beneficial. That being said, polars seems to be matching or beating data.table in performance, so I think it'd be very worth it to take it up. Wes McKinney, creator of pandas, has been quite vocal about architecture flaws of pandas -- which is why he's been working on the Arrow project. polars is based on Arrow, so in principle it's kinda like pandas 2.0 (adopting the changes that Wes proposed).
    9 projects | | 10 Nov 2021
    So the question is really - how is polars so fast? Polars is packed by Apache Arrow, which is a columnar memory format that is designed specifically for performance.
  • Comparing SQLite, DuckDB and Arrow
    5 projects | | 27 Oct 2021
  • The Data Engineer Roadmap ๐Ÿ—บ
    12 projects | | 19 Oct 2021
    Apache Arrow
  • C++ Jobs - Q4 2021
    4 projects | | 2 Oct 2021
    Technologies: Apache Arrow, Flatbuffers, C++ Actor Framework, Linux, Docker, Kubernetes
  • How to use Spark and Pandas to prepare big data
    3 projects | | 21 Sep 2021
    Pandas user-defined function (UDF) is built on top of Apache Arrow. Pandas UDF improves data performance by allowing developers to scale their workloads and leverage Pandaโ€™s APIs in Apache Spark. Pandas UDF works with Pandas APIs inside the function, and works with Apache Arrow to exchange data.
  • Announcing arrow-odbc
    2 projects | | 7 Sep 2021
    arrow-odbc allows you to iterate over an ODBC data source as sequence of Apache Arrow record batches.
  • CuVec: Unifying Python/C++/CUDA memory
    2 projects | | 18 Jul 2021
    IIRC Apache Arrow [1] promised similar goal and it seems covers CUDA as well [2]. I wonder how these relates in the big picture. This one seems much simpler than arrow, which is probably a good thing in terms of the differentiation?

    - [1]

    - [2]

  • Recommendation for a Database for analysis
    5 projects | | 13 May 2021
    What you need for your use case is a column-oriented store. I recommend explore bcolz or apache arrow for a column file-based systems. These are very fast, support memory mapping, uses compression and SSD speed (and even CPU architecture, in case of arrow) optimally almost out of the box, and has good interfaces to Numpy and Pandas (in case you are using Python for final data consumption and analysis). The columnar structure makes it easy to add or delete a column easily (or even dynamically). If you need a more scalable (albeit at the cost of speed) solution, you can devise a schema over a regular columnar db or an nosql db - see arctic from Man group for an example.

What are some alternatives?

When comparing perspective and Apache Arrow you can also consider the following projects:

h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.

polars - Fast multi-threaded DataFrame library in Rust and Python

arquero - Query processing and transformation of array-backed data tables.

ta-lib - Python wrapper for TA-Lib (

Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing

Apache HBase - Apache HBase

spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs

arrow-rs - Official Rust implementation of Apache Arrow

docker - These are the official Dockerfiles for

Arrow.jl - Pure Julia implementation of the apache arrow data format (

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