https://github.com/squey/squey

Squey is a visualization software designed to interactively explore and understand large amounts of tabular data (this is the read-only mirror of https://gitlab.com/squey/squey)
https://github.com/squey/squey

cybersecurity data-analysis data-science data-visualization exploratory-data-visualizations parallel-coordinates parquet parquet-files parquet-viewer pcap timeseries timeseries-analysis visualization

Last synced: about 2 months ago
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Squey is a visualization software designed to interactively explore and understand large amounts of tabular data (this is the read-only mirror of https://gitlab.com/squey/squey)

Host: GitHub
URL: https://github.com/squey/squey
Owner: squey
License: mit
Created: 2023-06-03T09:35:42.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2025-01-13T13:16:02.000Z (4 months ago)
Last Synced: 2025-01-13T14:25:45.084Z (4 months ago)
Topics: cybersecurity, data-analysis, data-science, data-visualization, exploratory-data-visualizations, parallel-coordinates, parquet, parquet-files, parquet-viewer, pcap, timeseries, timeseries-analysis, visualization
Language: C++
Homepage: https://squey.org
Size: 94.7 MB
Stars: 19
Watchers: 2
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-TS-anomaly-detection - Squey - depth exploration of timeseries while displaying every outliers. | MIT | :heavy_check_mark: (Related Software / Time-Series Analysis)

README

        [![Version](https://img.shields.io/flathub/v/org.squey.Squey?style=flat&color=blueviolet&label=version)](https://gitlab.com/squey/squey/-/raw/main/CHANGELOG)

[![Documentation](https://gitlab.com/inendi/inspector-reference-manual/badges/main/pipeline.svg?key_text=doc&key_width=30)](https://doc.squey.org)

[![Flathub installs](https://img.shields.io/flathub/downloads/org.squey.Squey?label=flathub&style=flat&color=blue)](https://flathub.org/apps/details/org.squey.Squey)

[![Matrix](https://img.shields.io/matrix/squey%3Amatrix.org)](https://matrix.to/#/#squey:matrix.org)

[![DOI](https://zenodo.org/badge/648945293.svg)](https://doi.org/10.5281/zenodo.13927905)

[![Liberapay](https://img.shields.io/liberapay/patrons/squey)](https://liberapay.com/squey)

# About

[![Squey presentation video](https://gitlab.com/squey/squey/-/raw/main/buildstream/files/screenshots/squey.gif)](https://www.youtube.com/watch?v=M9I8Vt5mmAc)

See presentation video : https://www.youtube.com/watch?v=M9I8Vt5mmAc

## Project description

Squey is designed from the ground up to take advantage of GPUs and CPUs to perform interactive explorations of massive amounts of data.


It gives users an exhaustive yet intuitive multi-view representation of columnar data and can ingest from:



    Structured text files (CSV, logs, ...)

    Apache Parquet files

    Pcap files

    SQL databases

    Elasticsearch databases



Squey strives to deliver value through its V.I.SU approach:




  

Visualize: Leverage various visual representations of raw data in combination with statistics

  

Investigate: Use filters to build an accurate understanding of millions of rows while switching instantly between capturing the big picture and focusing on the details

  

Spot the Unknown: As a structured understanding of the data emerges, identify unknowns and anomalies



Squey can be used for many different purposes, such as:



    

BI and Big Data: Bootstrap initial understanding of complex datasets and deep dive where necessary to design accurate data processing

    

Cybersecurity: Detect weak signals such as attacks and data leaks

    

IT troubleshooting: Resolve network issues and improve application performance

    

Machine Learning: Design training dataset to fulfill targeted improvements of Machine Learning models






Give yourself a chance to see your data and have fun exploring!


## Installation

### Linux

[](https://flathub.org/apps/details/org.squey.Squey)

```

flatpak install --user -y https://dl.flathub.org/repo/appstream/org.squey.Squey.flatpakref

flatpak run org.squey.Squey

```

### Windows

Windows support is available through the use of [WSL2](https://apps.microsoft.com/store/detail/windows-subsystem-for-linux/9P9TQF7MRM4R):

[](https://squey.gitlab.io/squey/squey_installer.exe)

### AWS

Deploying the software as a service on an AWS EC2 instance :

[](https://aws.amazon.com/marketplace/search/results?searchTerms=Squey)

## Reference manual

https://doc.squey.org

## Development

See the developement [README.md](buildstream/README.md) page.

### Installing a flatpak development branch

Note : Merge Requests having the `action::flatpak_export` [label](https://gitlab.com/squey/squey/-/labels#) are exported by the CI/CD pipeline as a flatpak package named after the git branch name.

Adding the flatpak development remote (once):

```

flatpak --user remote-add --no-gpg-verify squey_dev http://inspector-cassiopee.ensam.eu/flatpak

```

Installing a development branch:

```

flatpak install --user squey_dev org.squey.Squey//

```

### Roadmap

https://gitlab.com/groups/squey/-/roadmap

### Support the project

Use this DOI to make citations

[![Zenodo](https://zenodo.org/badge/648945293.svg)](https://doi.org/10.5281/zenodo.13927905)

Help the contributors to develop and maintain the software

[![LiberaPay](https://liberapay.com/assets/widgets/donate.svg)](https://liberapay.com/Squey/)

[![Stargazers repo roster for @squey/squey](https://reporoster.com/stars/dark/squey/squey)](https://github.com/squey/squey/stargazers)

ecosyste.ms

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https://github.com/squey/squey

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README